On the importance of time—a temporal representation of sound
Visual representations of speech signals
Coding time-varying signals using sparse, shift-invariant representations
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning Overcomplete Representations
Neural Computation
Encoding and decoding spikes for dynamic stimuli
Neural Computation
Continuous speech recognition with sparse coding
Computer Speech and Language
Auditory sparse representation for robust speaker recognition based on tensor structure
EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications
Dictionary learning for sparse approximations with the majorization method
IEEE Transactions on Signal Processing
Parametric dictionary design for sparse coding
IEEE Transactions on Signal Processing
Binaural sound localization based on sparse coding and SOM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Sound source localization using sparse coding and SOM
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Sparse coding for drum sound classification and its use as a similarity measure
Proceedings of 3rd international workshop on Machine learning and music
Real-time binaural sound source localization using sparse coding and SOM
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Generating motion trajectories by sparse activation of learned motion primitives
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Decomposition and dictionary learning for 3D trajectories
Signal Processing
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Nonstationary acoustic features provide essential cues for many auditory tasks, including sound localization, auditory stream analysis, and speech recognition. These features can best be characterized relative to a precise point in time, such as the onset of a sound or the beginning of a harmonic periodicity. Extracting these types of features is a difficult problem. Part of the difficulty is that with standard block-based signal analysis methods, the representation is sensitive to the arbitrary alignment of the blocks with respect to the signal. Convolutional techniques such as shift-invariant transformations can reduce this sensitivity, but these do not yield a code that is efficient, that is, one that forms a nonredundant representation of the underlying structure. Here, we develop a non-block-based method for signal representation that is both time relative and efficient. Signals are represented using a linear superposition of time-shiftable kernel functions, each with an associated magnitude and temporal position. Signal decomposition in this method is a non-linear process that consists of optimizing the kernel function scaling coefficients and temporal positions to form an efficient, shift-invariant representation. We demonstrate the properties of this representation for the purpose of characterizing structure in various types of nonstationary acoustic signals. The computational problem investigated here has direct relevance to the neural coding at the auditory nerve and the more general issue of how to encode complex, time-varying signals with a population of spiking neurons.