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Audio Mining
Audio mining is a technique by which the content of an audio signal can be automatically analyzed and searched. It is most commonly used in the field of automatic speech recognition, where the analysis tries to identify any speech within the audio. The term ''audio mining'' is sometimes used interchangeably with audio indexing, phonetic searching, phonetic indexing, speech indexing, audio analytics, speech analytics, word spotting, and information retrieval. Audio indexing, however, is mostly used to describe the pre-process of audio mining, in which the audio file is broken down into a searchable index of words. History Academic research on audio mining began in the late 1970s in schools like Carnegie Mellon University, Columbia University, the Georgia Institute of Technology, and the University of Texas. Audio data indexing and retrieval began to receive attention and demand in the early 1990s, when multimedia content started to develop and the volume of audio content significant ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis. Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker-independent" systems. Systems that use training are called "speaker dependent". Speech recognition applications include voice user interfaces ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Timbre
In music, timbre (), also known as tone color or tone quality (from psychoacoustics), is the perceived sound of a musical note, sound or tone. Timbre distinguishes sounds according to their source, such as choir voices and musical instruments. It also enables listeners to distinguish instruments in the same category (e.g., an oboe and a clarinet, both woodwinds). In simple terms, timbre is what makes a particular musical instrument or human voice have a different sound from another, even when they play or sing the same note. For instance, it is the difference in sound between a guitar and a piano playing the same note at the same volume. Both instruments can sound equally tuned in relation to each other as they play the same note, and while playing at the same amplitude level each instrument will still sound distinctive with its own unique tone color. Musicians distinguish instruments based on their varied timbres, even instruments playing notes at the same pitch and volume ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Music Information Retrieval
Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. Those involved in MIR may have a background in academic musicology, psychoacoustics, psychology, signal processing, informatics, machine learning, optical music recognition, computational intelligence, or some combination of these. Applications Music information retrieval is being used by businesses and academics to categorize, manipulate and even create music. Music classification One of the classical MIR research topics is genre classification, which is categorizing music items into one of the pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, instrument identification, and music tagging are also popular topics. Recommender systems Several recommender systems for music already exist, but surprisingly few are based upon MIR techniques, instead of making use of similarity between users or laborious data compilation. P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Recognition
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis. Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker-independent" systems. Systems that use training are called "speaker dependent". Speech recognition applications include voice user interfaces ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Machine Translation
Statistical machine translation (SMT) is a machine translation approach where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation, that superseded the previous rule-based approach that required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory. Statistical machine translation was re-introduced in the late 1980s and early 1990s by researchers at IBM's Thomas J. Watson Research Center. Before the introduction of neural machine translation, it was by far the most widely studied machine translation method. Basis The idea b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Music Information Retrieval
Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. Those involved in MIR may have a background in academic musicology, psychoacoustics, psychology, signal processing, informatics, machine learning, optical music recognition, computational intelligence, or some combination of these. Applications Music information retrieval is being used by businesses and academics to categorize, manipulate and even create music. Music classification One of the classical MIR research topics is genre classification, which is categorizing music items into one of the pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, instrument identification, and music tagging are also popular topics. Recommender systems Several recommender systems for music already exist, but surprisingly few are based upon MIR techniques, instead of making use of similarity between users or laborious data compilation. P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Analytics
Speech analytics is the process of analyzing recorded calls to gather customer information to improve communication and future interaction. The process is primarily used by customer contact centers to extract information buried in client interactions with an enterprise. Although speech analytics includes elements of automatic speech recognition, it is known for analyzing the topic being discussed, which is weighed against the emotional character of the speech and the amount and locations of speech versus non-speech during the interaction. Speech analytics in contact centers can be used to mine recorded customer interactions to surface the intelligence essential for building effective cost containment and customer service strategies. The technology can pinpoint cost drivers, trend analysis, identify strengths and weaknesses with processes and products, and help understand how the marketplace perceives offerings. Definition Speech analytics provides a Complete analysis of recorded pho ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linguistics
Linguistics is the scientific study of language. The areas of linguistic analysis are syntax (rules governing the structure of sentences), semantics (meaning), Morphology (linguistics), morphology (structure of words), phonetics (speech sounds and equivalent gestures in sign languages), phonology (the abstract sound system of a particular language, and analogous systems of sign languages), and pragmatics (how the context of use contributes to meaning). Subdisciplines such as biolinguistics (the study of the biological variables and evolution of language) and psycholinguistics (the study of psychological factors in human language) bridge many of these divisions. Linguistics encompasses Outline of linguistics, many branches and subfields that span both theoretical and practical applications. Theoretical linguistics is concerned with understanding the universal grammar, universal and Philosophy of language#Nature of language, fundamental nature of language and developing a general ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Music Information Retrieval
Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. Those involved in MIR may have a background in academic musicology, psychoacoustics, psychology, signal processing, informatics, machine learning, optical music recognition, computational intelligence, or some combination of these. Applications Music information retrieval is being used by businesses and academics to categorize, manipulate and even create music. Music classification One of the classical MIR research topics is genre classification, which is categorizing music items into one of the pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, instrument identification, and music tagging are also popular topics. Recommender systems Several recommender systems for music already exist, but surprisingly few are based upon MIR techniques, instead of making use of similarity between users or laborious data compilation. P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spectrograms
A spectrogram is a visual representation of the spectrum of frequencies of a signal as it varies with time. When applied to an audio signal, spectrograms are sometimes called sonographs, voiceprints, or voicegrams. When the data are represented in a 3D plot they may be called ''waterfall displays''. Spectrograms are used extensively in the fields of music, linguistics, sonar, radar, speech processing, seismology, ornithology, and others. Spectrograms of audio can be used to identify spoken words phonetically, and to analyse the various calls of animals. A spectrogram can be generated by an optical spectrometer, a bank of band-pass filters, by Fourier transform or by a wavelet transform (in which case it is also known as a scaleogram or scalogram). A spectrogram is usually depicted as a heat map, i.e., as an image with the intensity shown by varying the colour or brightness. Format A common format is a graph with two geometric dimensions: one axis represents time, and the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Deep Neural Networks
Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance fields. These architectures have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Naïve Bayes Classifier
Naivety (also spelled naïvety), naiveness, or naïveté is the state of being naive. It refers to an apparent or actual lack of experience and sophistication, often describing a neglect of pragmatism in favor of moral idealism. A ''naïve'' may be called a ''naïf''. Etymology In its early use, the word ''naïve'' meant "natural or innocent", and did not connote ineptitude. As a French adjective, it is spelled ''naïve'', for feminine nouns, and ''naïf'', for masculine nouns. As a French noun, it is spelled ''naïveté''. It is sometimes spelled "naïve" with a diaeresis, but as an unitalicized English word, "naive" is now the more usual spelling. "naïf" often represents the French masculine, but has a secondary meaning as an artistic style. "Naïve" is pronounced as two syllables, in the French manner, and with the stress on the second one. Culture The naïf appears as a cultural type in two main forms. On the one hand, there is 'the satirical naïf, such as Candide'. Nor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
