Chants and Orcas: semi-automatic tools for audio annotation and analysis in niche domains
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Identifying violin performers by their expressive trends
Intelligent Data Analysis - Machine Learning and Music
Machine Recognition of Music Emotion: A Review
ACM Transactions on Intelligent Systems and Technology (TIST)
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A Sawtooth Waveform Inspired Pitch Estimator (SWIPE) has been developed for processing speech and music. SWIPE is shown to outperform existing algorithms on several publicly available speech/musical-instruments databases and a disordered speech database. SWIPE estimates the pitch as the fundamental frequency of the sawtooth waveform whose spectrum best matches the spectrum of the input signal. A decaying cosine kernel provides an extension to older frequency-based, sieve-type estimation algorithms by providing smooth peaks with decaying amplitudes to correlate with the harmonics of the signal. An improvement on the algorithm is achieved by using only the first and prime harmonics, which significantly reduces subharmonic errors commonly found in other pitch estimation algorithms.