Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
On the segmentation and analysis of continuous musical sound by digital computer.
On the segmentation and analysis of continuous musical sound by digital computer.
Commuted Waveguide Synthesis of the Clavichord
Computer Music Journal
Sparse representations of polyphonic music
Signal Processing - Sparse approximations in signal and image processing
A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
Event based transcription system for polyphonic piano music
Signal Processing
Adaptive harmonic spectral decomposition for multiple pitch estimation
IEEE Transactions on Audio, Speech, and Language Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Specmurt Analysis of Polyphonic Music Signals
IEEE Transactions on Audio, Speech, and Language Processing
Multipitch Analysis of Polyphonic Music and Speech Signals Using an Auditory Model
IEEE Transactions on Audio, Speech, and Language Processing
A generative model for music transcription
IEEE Transactions on Audio, Speech, and Language Processing
A connectionist approach to automatic transcription of polyphonic piano music
IEEE Transactions on Multimedia
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Unsupervised analysis of polyphonic music by sparse coding
IEEE Transactions on Neural Networks
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Automatic transcription of polyphonic music is an important task in audio signal processing, which involves identifying the fundamental frequencies (pitches) of several notes played at a time. Its difficulty stems from the fact that harmonics of different notes tend to overlap, especially in western music. This causes a problem in assigning the harmonics to their true fundamental frequencies, and in deducing spectra of several notes from their sum. We present here a multi-pitch estimation algorithm based on sparse representations in a structured dictionary, suitable for the spectra of music signals. In the vectors of this dictionary, most of the elements are forced to be zero except the elements that represent the fundamental frequencies and their harmonics. Thanks to the structured dictionary, the algorithm does not require a diverse or a large dataset for training and is computationally more efficient than alternative methods. The performance of the proposed structured dictionary transcription system is empirically examined, and its advantage is demonstrated compared to alternative dictionary learning methods.