Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
The computer music tutorial
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Statistical Modeling of Co-Articulation in Continuous Speech Based on Data Driven Interpolation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
General sound classification and similarity in MPEG-7
Organised Sound
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Discriminative semi-parametric trajectory model for speech recognition
Computer Speech and Language
Interpolating hidden Markov model and its application to automatic instrument recognition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Structural Segmentation of Musical Audio by Constrained Clustering
IEEE Transactions on Audio, Speech, and Language Processing
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A computationally efficient algorithm is proposed for modeling and representing time-varying musical sounds. The aim is to encode individual sounds and not the statistical properties of several sounds representing a certain class. A given sequence of acoustic feature vectors is modeled by finding such a set of "states" (anchor points in the feature space) that the input data can be efficiently represented by interpolating between them. The proposed interpolating state model is generic and can be used to represent any multidimensional data sequence. In this paper, it is applied to represent musical instrument sounds in a compact and accurate form. Simulation experiments were carried out which show that the proposed method clearly outperforms the conventional vector quantization approach where the acoustic feature data is k-means clustered and the feature vectors are replaced by the corresponding cluster centroids. The computational complexity of the proposed algorithm as a function of the input sequence length T is O(TlogT).