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354
Automatic Musical Genre Classification Of Audio Signals
- IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING
, 2002
"... ... describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by sta ..."
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Cited by 829 (35 self)
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... describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.
Mel Frequency Cepstral Coefficients for Music Modeling
- In International Symposium on Music Information Retrieval
, 2000
"... We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) - the dominant features used for speech recognition - and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale ..."
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Cited by 299 (3 self)
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We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) - the dominant features used for speech recognition - and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spectral vectors.
Perceptual Coding of Digital Audio
- Proceedings of the IEEE
, 2000
"... During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applic ..."
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Cited by 158 (3 self)
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During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for high-quality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities. The paper is organized as follows. First, psychoacoustic principles are described with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the Modified Discrete Cosine Transform (MDCT), a perfect reconstruction (PR) cosine-modulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction (LP) parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of
An overview of audio information retrieval
, 1999
"... The problem of audio information retrieval is familiar to anyone who has returned from vacation to find an answering machine full of messages. While there is not yet an “AltaVista ” for the audio data type, many workers are finding ways to automatically locate, index, and browse audio using recent ..."
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Cited by 153 (1 self)
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The problem of audio information retrieval is familiar to anyone who has returned from vacation to find an answering machine full of messages. While there is not yet an “AltaVista ” for the audio data type, many workers are finding ways to automatically locate, index, and browse audio using recent advances in speech recognition and machine listening. This paper reviews the state of the art in audio information retrieval, and presents recent advances in automatic speech recognition, word spotting, speaker and music identification, and audio similarity with a view towards making audio less “opaque”. A special section addresses intelligent interfaces for navigating and browsing audio and multimedia documents, using automatically derived information to go beyond the tape recorder metaphor.
MARSYAS: A framework for audio analysis
, 2000
"... Existing audio tools handle the increasing amount of computer audio data inadequately. The typical tape-recorder paradigm for audio interfaces is inflexible and time consuming, especially for large data sets. On the other hand, completely automatic audio analysis and annotation is impossible using c ..."
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Cited by 142 (19 self)
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Existing audio tools handle the increasing amount of computer audio data inadequately. The typical tape-recorder paradigm for audio interfaces is inflexible and time consuming, especially for large data sets. On the other hand, completely automatic audio analysis and annotation is impossible using current techniques.
SoundSense: Scalable Sound Sensing for People-Centric Applications on Mobile Phones
"... Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone – a powerful sen ..."
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Cited by 139 (10 self)
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Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone – a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and SoundSense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept peoplecentric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users ’ everyday lives. Categories and Subject Descriptors
Content Analysis for Audio Classification and Segmentation,”
- in IEEE Transactions on Speech and Audio Processing,
, 2002
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Multimedia Content Analysis Using Both Audio and Visual Cues
, 2000
"... : Including all the scenes/shots that contain special events may generate too long an abstract. Also, simply staggering them together may not be visually or aurally appealing. In the MoCA project, it was determined that only 50% of the abstract should contain special events. The remaining part shoul ..."
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Cited by 118 (0 self)
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: Including all the scenes/shots that contain special events may generate too long an abstract. Also, simply staggering them together may not be visually or aurally appealing. In the MoCA project, it was determined that only 50% of the abstract should contain special events. The remaining part should be left for filler clips. The special event clips to be included are chosen uniformly and randomly from different types of events. The selection of a short clip from a scene is subject to some additional criteria, such as the amount of action and the similarity to the overall color composition of the movie. Closeness to the desired AV characteristics of certain scene types are also considered. The filler clips are chosen so that they do not overlap with the content of chosen special event clips, to ensure a good coverage of all parts of a movie. MPEG-7 Standard for Multimedia Content Description Interface MPEG-7 is an on-going standardization effort for content description of AV documen...
A comparative study on content-based music genre classification
- in Proc. SIGIR, 2003
"... Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre clas ..."
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Cited by 117 (17 self)
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Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs 1. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.
Features for Audio and Music Classification
- Proceedings of the International Symposium on Music Information Retrieval
, 2003
"... Four audio feature sets are evaluated in their ability to classify five general audio classes and seven popular music genres. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of th ..."
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Cited by 113 (5 self)
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Four audio feature sets are evaluated in their ability to classify five general audio classes and seven popular music genres. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of the features is analyzed and parameterized and these parameters are included as additional features. Using a standard Gaussian framework for classification, results show that the temporal behavior of features is important for both music and audio classification. In addition, classification is better, on average, if based on features from models of auditory perception rather than on standard features.
