Multiresolution spectrotemporal analysis of complex sounds.

By T. Chi, Powen Ru, Shihab A. Shamma. 2005

685 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Signal Processing

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Citations in the corpus

DDSP: Differentiable Digital Signal Processing (2020, background): “…human hearing has evolved to be highly sensitive to phase-coherent oscillation, decomposing audio into spectrotemporal responses through the resonant properties of the basilar membrane and tonotopic mappings into the auditory cortex (Moerel et al., 2012; Chi et al., 2005; Theunissen & Elie, 2014).”
Processing of Natural Sounds in Human Auditory Cortex: Tonotopy, Spectral Tuning, and Relation to Voice Sensitivity (2012, methodology): “Consequent operations mimic the computations performed by the auditory nerve and cochlear nucleus (Chi et al., 2005), resulting in the mathematical representation of sounds S in terms of an N F matrix W of coefficients, where N number of sounds, and F the number resulting frequency bins (N 60 and F ”
Reconstructing Speech from Human Auditory Cortex (2012, methodology): “We investigated an alternative, nonlinear model based on modulation (described in detail in [18]).”
Deep Scattering Spectrum (2013, methodology): “A scattering transform has striking similarities with physiological models of the cochlea and of the auditory pathway [20], [21], also used for audio processing [22].”
Automatic speech emotion recognition using modulation spectral features (2011, background): “On the other hand, advances in neuroscience suggest the existence of spectrotemporal (ST) receptive fields in mammalian auditory cortex which can extend up to temporal spans of hundreds of milliseconds and respond to modulations in the time-frequency domain (Depireux et al., 2001; Shamma, 2001; Chih”
Phase recovery, MaxCut and complex semidefinite programming (2012, background): “These moduli also provide physiological models of cochlear signals in the ear [ Chi et al., 2005] and recovering audio signals from wavelet modulus coefﬁcients is an important problem in this context.”
Phone recognition with hierarchical convolutional deep maxout networks (2015, background): “Neurophysiological studies found structures in the brain that respond to local spectrotemporal modulations [1], suggesting that the approach described above might indeed by a reasonable model of speech perception.”
Phase Retrieval Using Alternating Minimization (2013, background): “Inspired by the recent literature on trace norm relaxation of the rank constraint, they design SDPs to solve this problem.”
Neural processing of natural sounds (2014, background): “progress in understanding the nature of the auditory computations that are found in the ascending processing stream of both birds [30,41,48,51,58-61] and mammals [38,62-65].”
Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline (2013, background): “[10, 11, 12, 13, 14, 15, 16, 17, 18]), other metrics [19] as well as human performance could be investigated within our framework.”
Non-verbal sound processing in the primary progressive aphasias (2009, neutral): “Rather, spectral inversion alters more complex acoustic features, including spectral and joint spectrotemporal modulations that are likely to be critical for disambiguating natural from synthetic sounds (e.g. Chi et al., 2005).”
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy. (2018, neutral): “Compared to more traditional hand-engineered models (Chi et al., 2005; Dau et al., 1997; McDermott and Simoncelli, 2011), one disadvantage of our model is that individual units are less readily understood.”
PolyRNN: A time-resolved model of polyphonic musical expectations aligned with human brain responses (2025, unknown): “One way to interpret a piano roll is to see it as a simplified cochleogram, a time-resolved sound representation known to emerge from the cochlea and to be conveyed via the auditory nerve to the auditory cortex (Chi et al., 2005).”