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Semantic Audio
Semantic audio is the extraction of meaning from audio signals. The field of semantic audio is primarily based around the analysis of audio to create some meaningful metadata, which can then be used in a variety of different ways. Semantic Analysis Semantic analysis of audio is performed to reveal some deeper understanding of an audio signal. This typically results in high-level metadata descriptors such as musical chords and tempo, or the identification of the individual speaking, to facilitate content-based management of audio recordings. In recent years, the growth of automatic data analysis techniques has grown considerably, * Music Information Retrieval * Sound recognition * Speech segmentation * Automatic music transcription * Blind source separation * Musical similarity * Audio indexing, hashing, searching * Broadcast Monitoring * Musical performance analysis Applications With the development of applications that use this semantic information to support the user in ident ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Audio Signal
An audio signal is a representation of sound, typically using either a changing level of electrical voltage for analog signals, or a series of binary numbers for digital signals. Audio signals have frequencies in the audio frequency range of roughly 20 to 20,000 Hz, which corresponds to the lower and upper limits of human hearing. Audio signals may be synthesized directly, or may originate at a transducer such as a microphone, musical instrument pickup, phonograph cartridge, or tape head. Loudspeakers or headphones convert an electrical audio signal back into sound. Digital audio systems represent audio signals in a variety of digital formats.Hodgson, Jay (2010). ''Understanding Records'', p.1. . An audio channel or audio track is an audio signal communications channel in a storage device or mixing console, used in operations such as multi-track recording and sound reinforcement. Signal flow Signal flow is the path an audio signal will take from source to the sp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Metadata
Metadata is "data that provides information about other data", but not the content of the data, such as the text of a message or the image itself. There are many distinct types of metadata, including: * Descriptive metadata – the descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords. * Structural metadata – metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials. * Administrative metadata – the information to help manage a resource, like resource type, permissions, and when and how it was created. * Reference metadata – the information about the contents and quality of statistical data. * Statistical metadata – also called process data, may describe processes that collect, process, or produce st ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Music Information Retrieval
Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. MIR is a small but growing field of research with many real-world applications. 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 MIR is being used by businesses and academics to categorize, manipulate and even create music. Music classification One of the classical MIR research topic is genre classification, which is categorizing music items into one of 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 making use of similarity betwe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sound Recognition
Sound recognition is a technology, which is based on both traditional pattern recognition theories and audio signal analysis methods. Sound recognition technologies contain preliminary data processing, feature extraction and classification algorithms. Sound recognition can classify feature vectors. Feature vectors are created as a result of preliminary data processing and linear predictive coding. Sound recognition technologies are used for: * Music recognition * Speech recognition * Automatic alarm detection and identification for surveillance, monitoring systems, based on the acoustic environment * Assistance to disabled or elderly people affected in their hearing capabilities * Identifying species of animals such as fish and mammals, e.g. in acoustical oceanography Security In monitoring and security, an important contribution to alarm detection and alarm verification can be supplied, using sound recognition techniques. In particular, these methods could be helpful for intr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Segmentation
Speech segmentation is the process of identifying the boundaries between words, syllables, or phonemes in spoken natural languages. The term applies both to the mental processes used by humans, and to artificial processes of natural language processing. Speech segmentation is a subfield of general speech perception and an important subproblem of the technologically focused field of speech recognition, and cannot be adequately solved in isolation. As in most natural language processing problems, one must take into account context, grammar, and semantics, and even so the result is often a probabilistic division (statistically based on likelihood) rather than a categorical one. Though it seems that coarticulation—a phenomenon which may happen between adjacent words just as easily as within a single word—presents the main challenge in speech segmentation across languages, some other problems and strategies employed in solving those problems can be seen in the following sections. Th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Blind Source Separation
Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. It is most commonly applied in digital signal processing and involves the analysis of mixtures of signals; the objective is to recover the original component signals from a mixture signal. The classical example of a source separation problem is the cocktail party problem, where a number of people are talking simultaneously in a room (for example, at a cocktail party), and a listener is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a difficult problem in digital signal processing. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in thi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Musical Similarity
The notion of musical similarity is particularly complex because there are numerous dimensions of similarity. If similarity takes place between different fragments from one musical piece, a musical similarity implies a repetition of the first occurring fragment. As well, eventually, the similarity does not occur by direct repetition, but by presenting in two (or more) set of relations, some common values or patterns. Objective musical similarity can be based on musical features such as: Pitched parameters * Pitch interval similarity * Melodic similarity * Modulation pattern similarity * Timbral similarity Non-pitched parameters * Metrical structure similarity * Rhythmic pattern similarity * Section structure similarity Semiotic parameters * Modality structure similarity * Extensional similarity * Intensional similarity Nevertheless, similarity can be based also on less objective features such as musical genre, personal history, social context (e.g. music from the 1960s), and a 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 with the main benefit of searchability. 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Language Identification
In natural language processing, language identification or language guessing is the problem of determining which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods. Overview There are several statistical approaches to language identification using different techniques to classify the data. One technique is to compare the compressibility of the text to the compressibility of texts in a set of known languages. This approach is known as mutual information based distance measure. The same technique can also be used to empirically construct family trees of languages which closely correspond to the trees constructed using historical methods. Mutual information based distance measure is essentially equivalent to more conventional model-based methods and is not generally considered to be either novel or better than simpler techniques. Another technique, as described ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speaker Identification
Speaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to ''speaker recognition'' or speech recognition. Speaker verification (also called speaker authentication) contrasts with identification, and ''speaker recognition'' differs from '' speaker diarisation'' (recognizing when the same speaker is speaking). Recognizing the speaker can simplify the task of translating speech in systems that have been trained on specific voices or it can be used to authenticate or verify the identity of a speaker as part of a security process. Speaker recognition has a history dating back some four decades as of 2019 and uses the acoustic features of speech that have been found to differ between individuals. These acoustic patterns reflect both anatomy and learned behavioral patterns. Verification versus identification There are two major applications of speaker recognition techn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Shazam (service)
Shazam is an application that can identify music, movies, advertising, and television shows, based on a short sample played and using the microphone on the device. It was created by London-based Shazam Entertainment, and has been owned by Apple Inc. since 2018. The software is available for Android, macOS, iOS, Wear OS, watchOS and as a Google Chrome extension. The original UK developer of the app, Shazam Entertainment Limited, was founded in 1999 by Chris Barton, Philip Inghelbrecht, Avery Wang, and Dhiraj Mukherjee. On September 24, 2018, the company was acquired by Apple for a reported $400 million. Overview Shazam identifies songs using an audio fingerprint based on a time-frequency graph called a spectrogram. It uses a smartphone or computer's built-in microphone to gather a brief sample of audio being played. Shazam stores a catalogue of audio fingerprints in a database. The user tags a song for 10 seconds and the application creates an audio fingerprint. Shazam work ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
