Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Polychronization: Computation with Spikes
Neural Computation
Sinusoidal modeling using frame-based perceptually weighted matching pursuits
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Sinusoidal modeling of audio and speech using psychoacoustic-adaptive matching pursuits
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Anthropomorphic coding of speech and audio: a model inversion approach
EURASIP Journal on Applied Signal Processing
Sparse coding via thresholding and local competition in neural circuits
Neural Computation
Fast matching pursuit with a multiscale dictionary of Gaussianchirps
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Efficient parametric coding of transients
IEEE Transactions on Audio, Speech, and Language Processing
Union of MDCT Bases for Audio Coding
IEEE Transactions on Audio, Speech, and Language Processing
Unsupervised analysis of polyphonic music by sparse coding
IEEE Transactions on Neural Networks
On computational working memory for speech analysis
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
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This article deals with the generation of auditory-inspired spectro-temporal features aimed at audio coding. To do so, we first generate sparse audio representations we call spikegrams, using projections on gammatone/gammachirp kernels that generate neural spikes. Unlike Fourier-based representations, these representations are powerful at identifying auditory events, such as onsets, offsets, transients, and harmonic structures. We show that the introduction of adaptiveness in the selection of gammachirp kernels enhances the compression rate compared to the case where the kernels are non-adaptive. We also integrate a masking model that helps reduce bitrate without loss of perceptible audio quality. We finally propose a method to extract frequent audio objects (patterns) in the aforementioned sparse representations. The extracted frequency-domain patterns (audio objects) help us address spikes (audio events) collectively rather than individually. When audio compression is needed, the different patterns are stored in a small codebook that can be used to efficiently encode audio materials in a lossless way. The approach is applied to different audio signals and results are discussed and compared. This work is a first step towards the design of a high-quality auditory-inspired ''object-based'' audio coder.