Automatic audio segmentation using a measure of audio novelty

Foote, Jonathan

doi:10.1109/icme.2000.869637

Cited by 246 publications

(230 citation statements)

References 10 publications

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“…The problem of audio segmentation, also called novelty or change detection, has been widely studied, mainly for music and speech signals [1], [2]. This problem can be defined as finding time boundaries, called change points, which partition a sound signal into homogeneous and continuous temporal regions, called segments, that are inhomogeneous with the adjacent regions.…”

Section: A Backgroundmentioning

confidence: 99%

An Information-Geometric Approach to Real-Time Audio Segmentation

Dessein

Cont

2013

IEEE Signal Process. Lett.

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To cite this version:Arnaud Dessein, Arshia Cont. An information-geometric approach to real-time audio segmentation. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2013, 20 (4) Abstract-We present a generic approach to real-time audio segmentation in the framework of information geometry for exponential families. The proposed system detects changes by monitoring the information rate of the signals as they arrive in time. We also address shortcomings of traditional cumulative sum approaches to change detection, which assume known parameters before change. This is done by considering exact generalized likelihood ratio test statistics, with a complete estimation of the unknown parameters in the respective hypotheses. We derive an efficient sequential scheme to compute these statistics through convex duality. We finally provide results for speech segmentation in speakers, and polyphonic music segmentation in note slices.

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Section: A Backgroundmentioning

confidence: 99%

An Information-Geometric Approach to Real-Time Audio Segmentation

Dessein

Cont

2013

IEEE Signal Process. Lett.

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“…Structural analysis of song audio has been previously studied in several works, where the predominant technique has been, as suggested by Foote [10], to compute a similarity measure between all pairs of feature vectors corresponding to the song audio. Several researchers have developed techniques comparing feature vectors [11,12,13].…”

Section: Structural Analysismentioning

confidence: 99%

Mobile music modeling, analysis and recognition

Golik

Harb

Misra

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present an analysis of music modeling and recognition techniques in the context of mobile music matching, substantially improving on the techniques presented in [1]. We accomplish this by adapting the features specifically to this task, and by introducing new modeling techniques that enable using a corpus of noisy and channel-distorted data to improve mobile music recognition quality. We report the results of an extensive empirical investigation of the system's robustness under realistic channel effects and distortions. We show an improvement of recognition accuracy by explicit duration modeling of music phonemes and by integrating the expected noise environment into the training process. Finally, we propose the use of frame-to-phoneme alignment for high-level structure analysis of polyphonic music.

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“…For this we represent the harmonic content using chroma vectors using the method we proposed in [13]. In order to estimate the instant of changes between homogenous segments we use the "novelty score" proposed by [7]. This "novelty score" is obtained by convolving a Self-SimilarityMatrix (SSM) with a checkerboard kernel of size 2L.…”

Section: Harmonic Variation F Harmo (τ Tz)mentioning

confidence: 99%

Perceptual tempo estimation using GMM-regression

Peeters¹,

Flocon-Cholet²

2012

Proceedings of the Second International ACM Workshop on Music Information Retrieval With User-Centered and Multimodal Strategie

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Most current tempo estimation algorithms suffer from the so-called octave estimation problems (estimating twice, thrice, half or one-third of a reference tempo). However, it is difficult to qualify an error as octave error without a clear definition of what is the reference tempo. For this reason, and given that tempo is mostly a perceptual notion, we study here the estimation of perceptual tempo. We consider the perceptual tempo as defined by the results of the largescale experiment made at Last-FM in 2011. We assume that the perception of tempo is related to the rate of variation of four musical attributes: the variation of energy, of harmonic changes, of spectral balance and short-term-eventrepetitions. We then propose the use of GMM-Regression to find the relationship between the perceptual tempo and the four musical attributes. In an experiment, we show that the estimation of the tempo provided by GMM-Regression over these attributes outperforms the one provided by a stateof-the-art tempo estimation algorithm. For this task GMMRegression also largely outperforms SVM-Regression. We finally study the estimation of three perceptual tempo classes ("Slow", "In Between", "Fast") using both GMM-Regression and SVM-Classification.

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Automatic audio segmentation using a measure of audio novelty

Cited by 246 publications

References 10 publications

An Information-Geometric Approach to Real-Time Audio Segmentation

An Information-Geometric Approach to Real-Time Audio Segmentation

Mobile music modeling, analysis and recognition

Perceptual tempo estimation using GMM-regression

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