Modulation-Scale Analysis for Content Identification

Sukittanon, Somsak; Atlas, Les; Pitton, J.W.

doi:10.1109/tsp.2004.833861

Cited by 54 publications

(37 citation statements)

References 25 publications

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“…Methods addressing this problem have proposed critical band filtering to reduce acoustic frequencies, and a continuous wavelet transform instead of a Fourier transform [33], or a discrete cosine transform [13] for modulation frequencies. In [24], dimensionality reduction was performed either by averaging across modulation filters or across acoustic frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of speech from nonspeeech in broadcast news based on modulation frequency features

Markaki

Stylianou

2011

Speech Communication

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In audio content analysis, the discrimination of speech and non-speech is the first processing step before speaker segmentation and recognition, or speech transcription. Speech/non-speech segmentation algorithms usually consist of a frame based scoring phase using MFCC features, combined with a smoothing phase. In this paper, a content based speech discrimination algorithm is designed to exploit long-term information inherent in modulation spectrum. In order to address the varying degrees of redundancy and discriminative power of the acoustic and modulation frequency subspaces, we first employ a generalization of SVD to tensors (Higher Order SVD) to reduce dimensions. Projection of modulation spectral features on the principal axes with the higher energy in each subspace results in a compact set of features with minimum redundancy. We further estimate the relevance of these projections to speech discrimination based on mutual information to the target class. This system is built upon a segment based SVM classifier in order to recognize the presence of voice activity in audio signal. Detection experiments using Greek and U.S. English broadcast news data composed of many speakers in various acoustic conditions suggest that the system provides complementary information to state-of-the-art melcepstral features.

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Section: Introductionmentioning

confidence: 99%

Discrimination of speech from nonspeeech in broadcast news based on modulation frequency features

Markaki

Stylianou

2011

Speech Communication

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“…Özer et al use periodicity estimators and a singular value decomposition of the Mel frequency cepstrum coefficient (MFCC) matrix [3]. Sukkittanon and Atlas propose frequency modulation features [4]. These papers do not address the response to compression.…”

Section: A Systems That Use Features Based On a Single Bandmentioning

confidence: 99%

Distortion Estimation in Compressed Music Using Only Audio Fingerprints

Doets

Lagendijk

2008

IEEE Trans. Audio Speech Lang. Process.

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Abstract-An audio fingerprint is a compact yet very robust representation of the perceptually relevant parts of an audio signal. It can be used for content-based audio identification, even when the audio is severely distorted. Audio compression changes the fingerprint slightly. We show that these small fingerprint differences due to compression can be used to estimate the signal-to-noise ratio (SNR) of the compressed audio file compared to the original. This is a useful content-based distortion estimate, when the original, uncompressed audio file is unavailable. The method uses the audio fingerprints only. For stochastic signals distorted by additive noise, an analytical expression is obtained for the average fingerprint difference as function of the SNR level. This model is based on an analysis of the Philips robust hash (PRH) algorithm. We show that for uncorrelated signals, the bit error rate (BER) is approximately inversely proportional to the square root of the SNR of the signal. This model is extended to correlated signals and music. For an experimental verification of our proposed model, we divide the field of audio fingerprinting algorithms into three categories. From each category, we select an algorithm that is representative for that category. Experiments show that the behavior predicted by the stochastic model for the PRH also holds for the two other algorithms.

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“…In this approach a signal's components are treated as a product of a slowly varying modulator and a higher frequency carrier [76]. This modulation spectral approach allows recurrent decompositions into modulators and carriers, thus providing an approach to representation of signal change that integrates across multiple scales [78]. These methods for recognizing change are likely to be stronger than more conventional techniques, such as time domain: auto and cross correlation, AR, ARMA, ARIMA, or fARIMA models, time frequency: wavelet analysis, or fractal dimension analysis.…”

Section: E Detecting Inadequacies In the Validity Of A Model During mentioning

confidence: 99%

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

2006

Self Cite

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Modeling is essential to integrating knowledge of human physiology. Comprehensive self-consistent descriptions expressed in quantitative mathematical form define working hypotheses in testable and reproducible form, and though such models are always "wrong" in the sense of being incomplete or partly incorrect, they provide a means of understanding a system and improving that understanding. Physiological systems, and models of them, encompass different levels of complexity. The lowest levels concern gene signaling and the regulation of transcription and translation, then biophysical and biochemical events at the protein level, and extend through the levels of cells, tissues and organs all the way to descriptions of integrated systems behavior. The highest levels of organization represent the dynamically varying interactions of billions of cells. Models of such systems are necessarily simplified to minimize computation and to emphasize the key factors defining system behavior; different model forms are thus often used to represent a system in different ways. Each simplification of lower level complicated function reduces the range of accurate operability at the higher level model, reducing robustness, the ability to respond correctly to dynamic changes in conditions. When conditions change so that the complexity reduction has resulted in the solution departing from the range of validity, detecting the deviation is critical, and requires special methods to enforce adapting the model formulation to alternative reduced-form modules or decomposing the reduced-form aggregates to the more detailed lower level modules to maintain appropriate behavior. The processes of error recognition, and of mapping between different levels of model complexity and shifting the levels of complexity of models in response to changing conditions, are essential for adaptive modeling and computer simulation of large-scale systems in reasonable time.

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Modulation-Scale Analysis for Content Identification

Cited by 54 publications

References 25 publications

Discrimination of speech from nonspeeech in broadcast news based on modulation frequency features

Discrimination of speech from nonspeeech in broadcast news based on modulation frequency features

Distortion Estimation in Compressed Music Using Only Audio Fingerprints

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

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