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Human-Centered Systems are computer services that are implicitly, indirectly, and unobstructed to people whose primary goal it to interact

With other people. Computers are invisible – they act as electronic butlers. PD Email

It is a way to anticipate and meet people’s needs. Computers are thus introduced to a network of people interfacing with each other, instead of condemning.

Humans can operate in a loop computerized environment (see CHIL – Computers and the Human)

Interaction Loop [23]

Smart Room Environments are a new category of computer services. buy PD database for marketing

Computers monitor and interpret interactions and actions of people to help them communicate better. An example of an implementation is an automatic meeting support system. It tracks what was said, to whom and how.

It was [25]. Annotating speech recognition outputs with speakers’

The meeting notes can be properly indexed and skimmed. They can also be searched and retrieved for identity, attention, and emotion. Sociallysupportive workspaces [23] and augmented multiparty interactions (26] encourage cooperation among participants in meetings, with multimodal interfaces to enter.

Facilitate functionalities and manipulate the contributions of participants

Monitor group activities. Other services that are offered within the framework

CHIL [23] includes better ways to connect people and support human memory. Computers must automatically collect context-aware information, such as meeting type, topic, and environmental, in order to provide these services.

conditions and participant characteristics, such as attentional state.

Speech Translation is an example of computer-mediated software that supports human-to-human communication [27,28 and 29]. Speech is a task.

Translation is the recognition of incoming speech in the source language. The translator output is translated into the target language text.

Synthesize the translated text into audible speech in target language. Most Purchasing Department Email List

Applications are designed in two-directional one-directional systems. Some systems may be configured as such.

Automatic language identification is used to route the speech into the appropriate system [30]. The translation should preserve the original meaning of the spoken input but also reflect other aspects.

These include politeness, respect and directness. These are just a few of the many aspects.

This could be directly derived by speaker characteristics such as generation

Synthesized output that is appropriate based on speaker’s gender or other relevant factors.

The identification of an individual’s emotional state in order to interpret and communicate emotions. Some aspects of the human body are also important.

Relationship between the speaker/listener In some languages, the word “word” is used.

The hierarchy between the sender and the receiver can affect the usage of the form. In this case, the wrong form could offend the receiver. Japanese is one example of such an.

52 T. Schultz

For example, Dr. Sadaoki Tomuko could be addressed as Tomukosan if he is

If the sender is not the boss, a friend or Tomukosensei should be contacted. This is how to address it

Problem solved, the English-Japanese JANUS Translation System [31] was created to address this problem.

You can switch between politeness levels.

2.3 Adaptation to System Components

The classification of speaker characteristics is a critical component, as we have already discussed.

role in personalization and customization of applications. Speakers can also be used to customize and personalize applications.

To adapt components of the system to specific voice characteristics, it is necessary to assess the characteristics.

The content of the speech and the speaker. This adaptation has been completed.buy PD database for marketing

It has been shown to significantly improve recognition accuracy.

Compared favorably to overall system performance.

Traditional speech recognition adaptation is mostly concerned with the Purchasing Department Email List

The acoustic model and the language model can be adapted. The acoustic model was used in the early days.

An enrollment process that asked the user for permission to adapt was used.

Reading text prompts. This technique might prove to be very useful for power users.

The system allows you to store and preload speaker-specific acoustic models.

This enrollment process is tedious. You should therefore look for more recent information.

Systems rely on speaker adaptive learning methods which first determine the capabilities of the system. PD lists

The speaker’s identity is used to adapt the acoustic model based on that assumed identity. Some applications require a wider speaker class, such as gender.

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Pre-trained models to be loaded [32]. This is a dictionary.

Language model adaptation is the analysis of the topic or content of spoken input and then used to adapt [33]. Apart from speech recognition,

This technique can also be used to model other components of dialog. PD Email

Different dialog states or keywords that can trigger state switches.

Code switching is i.e. It is impossible to switch the language between utterances.

Monolingual speech recognition systems can handle this task. There have been efforts to

Multilingual speech recognition system is being developed [34]. But it seems favorable so far

To design language identification modules that direct speech input.

to the appropriate monolingual recognition software [30]. Idiolect has demonstrated to

Accent has a significant impact on speaker recognition [35].

This has been shown to be detrimental to speech recognition performance. These characteristics have been classified with great care.

and the correct adaptation of system components. We refer to the following:

The reader is urged to [36].

2.4 Summary Purchasing Department Email List

This section concludes with a table that summarizes the speaker characteristics most relevant for human-computer and user-centered applications. It also includes references to studies or implementation examples.

thereof. This section does not cover all the applications mentioned.

They are also described in great detail elsewhere in this issue. These include

53 Speaker Characteristics

Forensic applications where the characteristics gender, age and medical conditions are known. buy PD database for marketing

Language, accent, and sociolect all play an important role. Jessen provides a comprehensive overview of forensic applications in this issue [37]. We did not also discuss

Emerging applications for home parole, detection and fraud in the

context of Law Enforcement that are concerned with the speaker’s identity

emotion. This article provides an introduction to the field regarding emotion.

Eriksson, in this issue [38].

Table 1. Table 1.

Reference to Characteristic Applications PD lists

Identity Transaction Authentication [39] ; Access Control [8]

Dialog Systems [14]; Meeting Browser [25]

Gender Dialog Systems [32]; Speech Synthesis ([3]

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age Dialog Systems [32]; Forensics [37]

Speech Synthesis [19]

health Forensics [37] PD email database

Language Call Routing [15] ; Speech Translation [30]

dialect Forensics [37]

Accent Language Learning [21]; Dialog Systems

Speech Synthesis [19] ; Forensics[37]

Assessment Systems [20]

sociolect Forensics [37]

idiolect Speaker Recognition [35]; Forensics [37]

emotional state Translation [40]; Meeting browser [25] Purchasing Department Email List

Law Enforcement [38]; Dialog Systems (18,17]

Attentional state Human-Robot Interaction [41] Smart Workspaces (26,23,24]

Relationship/role Translation [31] PD database for sale

Cultural background Dialog Systems [22]

3 What? 3 What?

These are the discrete speaker classes to which vectors for speech features are assigned.

A speaker’s characteristics. These characteristics are relevant for speech-based applications. We have created a hierarchical structure.

As described above.

Figure 1 illustrates the proposal taxonomy. It distinguishes first and foremost between psychological and physiological aspects of speaker characteristics. The

These aspects are further sub-divisioned into those that concern each speaker

Contrary to those that are specific to a community or group. For

A speaker could be a professor at university, a wife or mother to her children, or both. The authority of a

The context in which the speaker speaks may be different. The hierarchy is determined by the people he/she talks to. Credibility may also depend on the person being done. Purchasing Department Email List

54 T. Schultz

Speaker Characteristics

Psychological

Individual collective identity, gender health, age PD database for sale

Geographic

Background

Attention

State

Emotional

State

Relationship role

language accent dialect

idiolect sociolect

Fig. 1. Taxonomy of Speaker Characteristics

business with, etc. The definition of the group “collective” is required.

A relationship between the sender and the receiver.

This taxonomy has its limitations. It does not include all subjects. PD lists

Aspects of an individual (e.g. Weight, height, smoking and drinking habits, demographics like race, income mobility, employment status, or other special aspects

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Speech pathologies are an example of this, but instead we should be focusing on the characteristics that we think are important.

To be applicable (and assessable in the context typical speech applications. PD email database

The taxonomy also does not indicate which levels of linguistic information are required to distinguish between different characteristics. This is an example:

Low-level acoustic features can usually be used to distinguish gender; phonetic, however, is often sufficient.

To discriminate between idiolects, phonological and lexical knowledge may be necessary.

It needs syntactic and semantic information to distinguish sociolects.

To understand the role of speakers, and their role in society, pragmatics may be required. PD database for sale

Relationship to a group. Low-level physical aspects can be relatively simple

High level cues, which are hard to extract automatically, can be difficult to assess. This is a result.

Most automatic systems for speaker recognition are still focused on low-level speakers.

cues. Purchasing Department Email List

The taxonomy does not include discrimination.

between stable versus transient characteristics. Speaker identity and gender are examples of stable characteristics. Transient characteristics can change over time

time. This aspect could be important for practical applications, particularly if it is a characteristic that underlies dynamic changes over the course of a single period.

audio recording session. Although there are locally stable characteristics like age and health,

Language, accent, dialect, and even idiolect can change much more slowly than the others.

Duration of the recording session, characteristics like attentional and emotional

55 Speaker Characteristics

The state of the speaker and the context or topic changing dynamically. The

Over the course of an interaction, the relationship between a speaker and the listener can change. The collective may also have other characteristics, such as sociolect.

The spoken language has many functions, including dialect, accent, and dialect.

It is quite stable within the same language. If a speaker changes languages during a recording session, then the class assignments for accent, idiolect and idiolect will be changed.

Both the dialect and English language can often change. PD address lists

3.1 Language-Dependent Speaker Characteristics

The following outlines the five characteristics language accent dialect.

Language-dependent speaker characteristics include sociolect and idiolect. They are somewhat dependent upon the language used by the speaker.

speaker.

The line between genuinely different languages and dialects of English is drawn

The same language can be the subject of many disputes. We define a dialect as a regional Purchasing Department Email List

Modifications at the lexical or grammatical levels are a variant of a language. Accent, on the other hand, is a regional variant that affects only pronunciation. It mainly affects phonetic realizations, but also prosody and allophonic distribution.

Fluency and grammar. For example, British Received Pronunciation is an accent on English

Scottish English, on the other hand, would be considered a dialect because it frequently exhibits

Grammatical differences such as “Are you no going?” or “Aren’t you going?”

(see [42]). It is assumed that dialects of the same language can be understood by each other. PD address lists

Different languages may not be the same, but they are different. Languages need to be learned explicitly, even though they may not be the same.

Language speakers from other languages. Languages also have their own literary characteristics.

Tradition, dialects are primarily spoken varieties of languages without any literary tradition.

These definitions have been greatly simplified. Many languages do not have a written system, and therefore lack any literary tradition. The distinction between

Languages and dialects are a continuum, not a binary choice. They are often motivated by sociopolitical rather that linguistic considerations. Chinese

Languages, for instance, are unified by a common writing system. However they have their own distinct syntax. PD mailing lists

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a large number of unintelligible and mutually incompatible varieties, which differ significantly in grammar, vocabulary, pronunciation, and grammar. Most linguists will argue that PD email database

These variations can be considered different languages and are officially called dialects.

Encourage the idea of Chinese national unity (see “42”)). The exact opposite

This happened for Serbo­Croatian (the official language of the former Yugoslavia). After

After the split, the languages of Croatian and Serbian were referred to as

Separate languages to highlight national independence

Languages exhibit sociolectal and idiolectal variations in addition to regional variations.

variation. An idiolect is a speech pattern that has a consistent pronunciation, lexical choice or grammar and which is specific to one speaker. Some idiolectal patterns can include speaker-specific repetitive phrases (e.g. a tendency

To start sentences with Well, to put it simply …), characteristic intonation pattern,

or divergent pronunciations (e.g. nucular instead of nuclear (see [42]). A sociolect is a collection of variations that are typical of a defined group of speakers.

Not by regional cohesion, but by social parameters like economic status and age PD address lists

56 T. Schultz

profession, etc. Some dialects can be considered both a sociolect and a dialect, as they often reflect a specific social status. You can take, for example:

Standard German is very similar to dialects spoken in Hannover or the Purchasing Department Email List

state of Saxony–Anhalt, which is the source of Martin Luther’s bible

Translation was the foundation for standard German development. Thus,

Standard German, while being a dialect in these specific areas, is also a sociolect in the sense that it carries a certain prestige as the national language.

Germany is used in all aspects of broadcasting, press and by citizens throughout the country.

higher education.

Despite all the efforts made to make speech recognition systems more robust for real-world applications, regional variations remain a major problem.

challenge. Non-native words can lead to significant increases in word error rates

[43,44] & dialectal speech [45]. This performance is a result of one thing.

Degradation is when acoustic models are customized and pronunciation dictionaries are targeted PD email database providers

towards native speakers and lacks the variety that comes from non-native pronunciations. The lexicon as well as the language model do not include dialectal variety. The

The straightforward solution to deploying accent- or dialect-specific speech recognizers is not possible due to two limitations: a lack of platform resources, and a lack of infrastructure.

Data. Mobile or automotive applications are particularly embedded and restrict the integration of multiple recognizers in one system. Even if

Resources permit the deployment dialect- or accent-specific systems. Purchasing Department Email List

This results in very limited data resources. Real-world applications therefore require cross-dialect recognition or non-native recognition. Refer to the reader

For a complete introduction to this area, see [36]. You can find idiolectal features here. PD mailing lists

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This is used to tailor a speech application for a particular user, such as in training

A speech-based, automated office assistant. Additionally, the software includes idiolectal capabilities. PD email listing

[35] These features have been proven to be useful in automatic speaker identification. When developing an application, it is also possible to consider sociolectal characteristics.

an entire user group.

Individual

Accent

Phonetic Lexical

Grammar

k idiolect: l

Language

Collective

k sociolect l

Language k

Fig. 2. Language-dependent Characteristics PD email database providers

Speaker Characteristics 57

Multilingual environments may have an impact on idiolectal or sociolectal variations. For example, [46] has evidence that bilingual speakers alter their L1 speech after they speak in multilingual settings. Purchasing Department Email List

Spending time in a L2-speaking environment. There are many techniques that can improve your speech

Recognition performance in the presence code-switching has been studied [47,48]. The act of using words and phrases from code-switching is known as code-switching.

Multilingual speakers often use different languages in the same sentence.

Engaged in informal conversations

Figure 2 shows the similarities and differences between the languagedependent characteristics language dialect accent, sociolect, idiolect and accent. Main

The effects of linguistic aspects on discriminating factors and whether they are relevant to the individual, are considered discriminating factors.

Individuals and groups can have the same characteristics.

4 How? 4 How?

Characteristics

The most prominent and widely studied task in investigating the is probably the.

“Assignment speech features to discrete speakers classes” is speaker recognition

(Who is speaking, class=identity) & language identification (which language).

spoken, class=language). Speech recognition (what is being said, class=content) addresses buy PD database online

This is a larger problem that could be considered part of the “Speaker Classification”

When high-level characteristics such as topic, content, or role are being investigated. As it has become apparent that there are solutions to these three tasks, they have grown closer. Purchasing Department Email List

One task might be more beneficial than the other and all three must be done together.

Study of speech-based real-world applications to improve speech quality The following are the results.

We will briefly discuss speaker recognition and language identification. This section

It is not intended to be a complete introduction. For more information, the reader should refer to

Refer to in-depth overviews such as [49] language identification and [39.50] for language classification.

For speaker recognition. This article is a good introduction to speech recognition. PD mailing lists

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4.1 Speaker Recognition

The level of linguistic understanding applied to solving the classification task can help distinguish between different classification approaches. Reynolds identifies a hierarchy perceptual cues humans use to recognize speakers. PD email listing

[39]. At the highest level, people use semantics and diction.

These are ideosynchrasies that arise from socio-economic status and education.

the place of birth of the speaker. Prosodic features are located on the second level.

The characteristics of personality and parental influence are rhythm, speed and intonation. People are at the lowest level of linguistic communication.

Use the acoustic aspect of sounds such as nasality and breathiness to determine their authenticity.

Let’s draw some conclusions about the anatomical structure and vocal range of the speaker.

apparatus. Low level physical cues can be extracted automatically. However, it is difficult to determine high-level cues. Most automatic systems are therefore able to detect high-level cues.

Systems for speaker recognition still focus on low-level cues.

58 T. Schultz

Conventional systems use Gaussian Mixture Models to capture Purchasing Department Email List

frame-level characteristics [52]. GMMs are often unable to distinguish speaker-specific information that changes over multiple frames, as speech frames are presumed to be independent of each other. GMMs do not perform well. buy PD database online

These are able to discriminate speakers based upon higher-level differences such as idiolect. GMMs can also be affected by mismatched acoustics

Conditions that rely solely on low-level speech signal features. To overcome

These problems have led to speaker recognition focusing on higher-level linguistic features such as phonetic information emerging out of speaker ideosynchrasies [35]. This is known as phonetic speaker recognition. It applies relative

Frequency from phone ngrams [53]. This method is being intensively researched [39] and further developed by various modeling strategies, variations in statistical

n-gram models [54], various classifiers such as Support Vector Machines [55],

Modeling cross-stream dimensions to uncover underlying phone dependencies across multiple language [54,56].

4.2 Language Identification

Language identification methods can be classified in the same way as speaker recognition. This classification is based on the level of linguistic data.

task. [49] distinguishes the signal processing level, and the unit level (e.g. phones),

The sentence level is the word level. These levels allow him to distinguish between spectral features-based acoustic approaches and language identification.

derived from speech segments [57], Phonotactic approaches that use the contraints relative frequencies of sound unit [58], and other derivatives

Multilingual phone recognizers used as tokenizers [59], extended N-grams [60],

Cross-stream modeling [61], as well as combinations of GMMs, phonotactic and phonotactic model [62]. Navr’atil [49] also lists prosodic approaches that use tone

Intuition, prominence, and prominence [63], as well as those approaches that use full speech buy PD database online

Recognition of language identification [64]

5 A Classification System For Speaker Characteristics

This section presents a general classification system that applies one framework to the classification and classification of different speaker characteristics. Purchasing Department Email List

Identity, gender, accent, proficiency, level of attention, and language

speaker. Framework uses high-level phonetic information in order to capture speakers’ ideasynchrasies. This was originally proposed by [58] within the context of language.

Identification and [35] in context of speaker recognition. The idea behind the basic concept is to

To decode speech using various phone recognizers, and to use the relative frequency of

Phone n-grams are features for training speaker characteristics models and their

classification. By using more language-independent languages, we enrich existing algorithms with the application of different speaker characteristics.

Phone recognizers and modeling dependencies across multiple phone channels PD quality email lists

[54]. We also investigate the impact of different decision rules and examine their implications.

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Speaker Characteristics 59

Consider the number of languages involved and compare multilingual to multi-engineering. PD email listing

Approaches with respect to classification performance

5.1 Multilingual Phone Sequences

Our experiments were done using the GlobalPhone phone recognition software

Project [65] is available in 12 languages Arabic (AR), Mandarin Chinese(CH),

Croatian (KR), German, French (DE), Japanese (JA), Korean [KO], Portuguese (PO), Russian (“RU”), Spanish (SP), Swedish “SW”) and Turkish (“TU”)

These phone recognizers were trained with the Janus Speech Recognition PD email id list

Toolkit. The acoustic model is composed of a context-independent, 3-state HMM

16 Gaussians per State in the system. The 13 Mel-scale is the basis of the Gaussians

Cepstral power and coefficients, with first- and second-order derivatives. These are the following Purchasing Department Email List

Cepstral subtraction, Linear Discriminant Analysis reduces the input vector

Up to 16 dimensions. Vocal tract length normalization (VTLN), is a part of training.

Normalization of speaker sounds To decode, unsupervised MLLR is used to locate the

Best matching warp factor to the test speaker. The decoding process is done with

Viterbi searches using a fully connected network of monophones null-grammar networks

i.e. The recognition process does not require any prior knowledge of phone statistics.

Figure 3 illustrates the relationship between phone unit count and phone error.

Rates for ten languages

A language dependent phonetic model (n-gram) is created to train a model for a specific speaker characteristic. It is based on available training data.

We train phonetic bigram models based on the CMUCambridge Statistical language model toolkit [19]. Phonetic bigram models can be directly estimated using the data and not by applying universal background models, or adapting with background models. No transcriptions PD email id list

Speech data is required for any stage of model training. Figure 4 illustrates the

Procedure for training a speaker identity model speaker k.

Phone recognizers (PR1 ,…,PRm), decode speaker k’s training data to create m phone strings. These phone strings are used to create m phonetic bigram models.

(PM1,k ,…,PMm) are estimates for speaker k.

needs to be classified as one of the following: n-class speaker characteristic, m phone

Recognition software will produce models of m x 10 phonetic bigrams.

Each of the PRi m-phone recognizers, which are used to train phonetic bigram models, decodes the audio segment. Each of the resulting M phone strings is scored against one of the n bigram model PMi,j. This Purchasing Department Email List

This results in a perplexity matrix PP. The PPij element of the phonetic bigram model PMi on the phone string output is phone

Recognizer PRi We will investigate other options in future experiments. PD email id list

Our default decision algorithm is C* to suggest a class estimate

Select the j

Lowest

i(PP)i,j . This procedure is illustrated in Figure 5. We refer to it as MPM-pp.

The MPM-pp classification method is used in the following.

There are many classification tasks that can be done in relation to speaker characteristics.

Language classification

This section applies the MPM-pp framework for the multiclassification problem in four languages: Japanese, Russian (RU), Spanish and Spanish (SP).

Turkish (TU) We used a limited number of phone recognitions in PD quality email lists

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Languages other than the four languages of classification are used to duplicate the

Common circumstances for our identification experiments and to show a PD email leads

Language independence that is maintained even when the language is identified

65 Speaker Characteristics

Table 8. Confusion matrix for 3-way proficiency classification using 6 versus 7 phone

Recognizers

Phonetic 6 languages 7 langues

model C-1 C-2 C-3 C-1 C-2 C-3

C-1 8 3 19 8 5 17

C-2 8 41 61 6 53 51

C-3 2 12 99 1 20 92

domain. Phone recognizers in Chinese, German, and French

With phone vocabulary sizes of 145 to 47 and 42 respectively, these were borrowed from

The GlobalPhone project. Data for this experiment were also available.

It was borrowed from GlobalPhone but not used for training phone recognizers. It was broken up as shown in Table 9 Training was done with data set 1.

The phonetic models were used, and data set 4 was not available during training. PD email id list

It is used to assess the performance of the entire classifier. Data

Sets 2 and 3 were used to experiment with different materials.

Decision strategies

Table 9. Table 9.

Set JA RU SP TU

nspk 1-20 20 20 20/20 20

2 5 10 9 10

3 3 5 5 5

4 3 5 4 5

nutt all 2294 4923 2724 2924 Purchasing Department Email List

tutt all 6 hrs 9 hrs 8 hrs 7 hrs

To train the phonetic bigram models, you will need to utterances starting from set 1 in each Lj

Each of the three phone recognition algorithms PRi was used to decode JA, RO, SP, and TU

CH, DE, FR. Twelve trigram models with Kneser/Ney were created

No cut-off or backoff. The size of the training corpora varied from 140K to 500,000.

250K tokens. All 12 models had trigram coverage of 73% to 95%.

Unigram coverage less than 1%

Our lowest average perplexity decision was the first to benchmark accuracy.

rule. We constructed a 4-class multi-classifier using the same principles.

Data set 2 was for each of the durations tk: 5s, 10s and 20s respectively; data set 3 was

Cross-validation

Multi-classifier combines the outputs of multiple binary classifiers. buy PD email database

ECOC is error-correcting output coding. A class space that contains 4 languages produces 7 binary partitions. Each of these was taught an independent instructor.

Multilayer Perceptron (MLP), 12 input units, 1 output unit that uses scaled

Conjugate gradients for data set 2 and early stopping with cross-validation

Crossover

Passive crossover networks are built using capacitors, inductors, or both.

Resistors are used to split the audio signal for high-frequency information

goes to the low-frequency information and high-frequency driver

The woofer is the winner. Passive crossovers offer the best value for money

Allow the amplifier to power the loudspeaker PD quality email lists

channel. Passive crossovers work in most cases but are not recommended. Purchasing Department Email List

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They are less precise than active crossovers.

To operate, active crossovers need external power. They can be powered by external power.

The output of a mixer or preamplifier is connected to the output of an amplifier. PD email leads

Power amplifier. Active crossover has the advantage of being able to control power amplifiers.

You can change many characteristics, such as the crossover frequency.

The rate of transition.

A active crossover is usually an additional component you will need

To purchase. Check that your loudspeaker system is certified.

Separate inputs are needed to accommodate multiple amplifiers.

Your crossover. An especially cost-effective method to obtain an

Active crossover can be achieved by purchasing a Digital Signal Processor, (DSP).

You can create equalizers and crossovers. buy PD email database

Woofer

Bosch Security Systems 9 (c) 2012 Bosch Security Systems Purchasing Department Email List

The signal is sent through a loudspeaker…or more.

The signal chain contains the audio signal.

Use an instrument or microphone to communicate your message.

The Signal Chain

A sound system is a system that transmits an audio signal from its source to the system components. These components balance, process and amplify the signal.

A loudspeaker, or headphones, is released. The signal chain is the audio signal’s path.

Some signal chains can be complex with many components and divergences. Others are simple. Below is a diagram of a signal chain.

Basic, stripped-down signal chains

What Does an Audio System Look Like?

(c) 2012 Bosch Security Systems 10.

What Signal Voltage is and why it matters

Signal voltage refers to the value of the signal being sent to an audio device. We usually refer to the value in decibels volts (dBV).

Each microphone, amplifier, pickup, and source of program has a unique voltage level. However, the signals cannot be mixed until they are all combined. Purchasing Department Email List

They are all promoted to “line” professional status. buy PD email database

Mic level is the relatively

Low-level signal (generally)

From -40 to -60 dBV (of a)

Microphone that must be

Amplified to line-level,

It is easier to manipulate purchase PD email lists

A mixing console.

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Different levels of signals

Enter a mixer, but make sure the signal is clear

Professionals can take your leaves. PD email leads

line level. The standard is

Audio: +4 dBu audio or -10 dBV

Levels, or about 1V.

We need stronger signals

to drive loudspeakers.

Amplifiers boost processed

Signals to the speaker level

So the loudspeakers are rewarded

There is enough power to get the job done. Purchasing Department Email List

(c) 2012 Bosch Security Systems

11

Coverage Pattern is a directional pattern for a loudspeaker system that can be varied by frequency or tone. One example is a loudspeaker’s

One output may cover a large and narrow area of a room while another might cover an equally long and tall section.

Pro Tip:

Make sure your loudspeakers cover your entire audience. People who are unable to hear you clearly are more likely not to be able to hear you.

Throw things on the stage. email marketing database PD

Attachment: A cabinet that houses loudspeaker drivers as well as associated electronic hardware such crossover circuits. An

An enclosure can be as simple as a wooden box, or it could be complex and contain ports, baffles and acoustic insulation. The enclosure

This prevents sound waves from interfering between the rear and front, reduces vibration and heat generation.

Driver coils. It also shapes the low frequency response. To do this, the enclosure design must be compatible with the driver characteristics.

This was achieved with great success. Electro-Voice was the first company to implement the concept of matching enclosure designs systematically.

Drivers should be able to extend the low end as much as possible. Purchasing Department Email List

Frequency Response is a measurement of how well a speaker or electronic component reproduces sounds. Frequency: A frequency

Response specification refers to the frequency range and deviation from a perfectly flat response.

Frequency response curves can be used to show the accuracy of sound systems.

Frequency Range is the lower and higher limits of the system’s output. It is not possible for a loudspeaker to reproduce the sound of another speaker.

Anything below or over its rated frequency range is unacceptable, Buddy.

Pro Tip: email marketing database PD

Different speaker manufacturers may use different standards. To make an accurate comparison, be sure to read the manufacturer’s footnotes.

The frequency range of a frequency measured at the -10 dB points will usually be larger than that measured at the 3 dB points. However, it is not fair.

Considerations when choosing a loudspeaker

Here are some helpful terms

You’ll be able to explore your options for loudspeakers and you’ll see many fancy terms such as frequency, impedance, and frequency.

Response, power rating, SPL. They are, however, very important. Here’s a quick primer.

Bosch Security Systems 12 (c) 2012 Bosch Security Systems

Impedance (Z), The resistance that an electronic circuit or device offers to the AC current flowing through it

It. It is often represented by the mathematical symbol Z and measured in Ohms. Impedance

This article describes the difficulty of a speaker to drive and its compatibility for various amplifiers. purchase PD email lists

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Pro Tip:

Before you purchase your loudspeakers, make sure to check the compatibility of your amplifiers. We are here to help.

Power Rating: Peak and continuous power ratings can be measured separately. Continuous indicates how much. PD email Profile

The power that the loudspeaker can handle over time. Peak shows you how much power the loudspeaker can handle in short bursts.

In other words, you shouldn’t expect to melt your faces at the peak rating for very long. Many manufacturers do also.

A “life test” is used to assess how well a loudspeaker will withstand abuse. Every loudspeaker is subject to a 100-hour testing at EV.

Loudspeaker used to confirm the continuous power handling measurement.

Sound Pressure Level (SPL), The volume of an acoustic sound expressed in decibels (dB). Maximum SPL

This is the maximum sound pressure level that a loudspeaker can tolerate before distortion occurs. Purchasing Department Email List

A Few (or More) Helpful Terms

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It’s time for the professionals to help you when you’re in charge of a show like this.

What number of loudspeakers do I need?

One sound system is the most basic. email marketing database PD

loudspeaker. That’s fine for some people. If the venue is

Cozy (for example, when you are playing a didgeridoo show at a coffee shop).

Shop), one loudspeaker might be all you need.

The louder the speakers, the larger the venue.

need.