A computationally efficient multipitch analysis model

By Tero Tolonen, Matti Karjalainen. IEEE Transactions on Speech and Audio Processing, 2000

391 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4d247b8b6382e50dd169b7226baa14c90961ec23

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Citations in the corpus

Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle (2010, positive): “It can be achieved thanks to the MA estimation approach proposed in [23].”
Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres (2011, methodology): “Pitch uses an estimation of the centroid and deviation of the unwrapped chromagram, and also in parallel a statistic description of pitch component based on advanced pitch extraction method (Tolonen & Karjalainen, 2000).”
A Probabilistic Interaction Model for Multipitch Tracking With Factorial Hidden Markov Models (2011, background): “Methods for multipitch tracking of speech include [1], [15], [16], [17] and recently [18].”
Multiple fundamental frequency estimation based on harmonicity and spectral smoothness (2003, methodology): “A third, psychoacoustically motivated group of algorithms measures the periodicity of the amplitude envelope of a time-domain signal within several frequency channels [20], [21], [39].”
Musical genre classification of audio signals (2002, methodology): “The autocorrelation function is enhanced using a similar method to the multipitch analysis model of Tolonen and Karjalainen [22] in order to reduce the effect of integer multiples of the basic periodicities.”
A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “For frame level pitch analysis, various multi-pitch estimation algorithms have been developed [94], [95] to identify the top candidate pitches for each”
A multi-pitch tracking algorithm for noisy speech (2002, background): “In fact, there is a considerable range of appropriate values for these parameters, and overall system performance is not very sensitive to the specific parameter values used.”
Factors in automatic musical genre classification of audio signals (2003, methodology): “The features used to represent timbral texture are based on standard features proposed for music-speech discrimination and speech recognition.”
A real-time music-scene-description system: predominant-F0 estimation for detecting melody and bass lines in real-world audio signals (2004, methodology): “We think that this decomposition behavior is the advantage of PreFEst in comparison to previous comb-filter-based or autocorrelation-based methods (de Cheveigné, 1993; de Cheveigné and Kawahara, 1999; Tolonen and Karjalainen, 2000).”