Automatic chord recognition from audio using a supervised HMM trained with audio-from-symbolic data
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
The Cognition of Basic Musical Structures
The Cognition of Basic Musical Structures
Automatic chord recognition from audio using a supervised HMM trained with audio-from-symbolic data
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
A System for Automatic Chord Transcription from Audio Using Genre-Specific Hidden Markov Models
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Genre classification using chords and stochastic language models
Connection Science - Music, Brain, Cognition
Genre classification of music by tonal harmony
Intelligent Data Analysis - Machine Learning and Music
On the Relative Importance of Individual Components of Chord Recognition Systems
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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A novel approach for obtaining labeled training data is presented to directly estimate the model parameters in a supervised learning algorithm for automatic chord recognition from the raw audio. To this end, harmonic analysis is first performed on symbolic data to generate label files. In paral-lel, we synthesize audio data from the same symbolic data, which are then provided to a machine learning algorithm along with label files to estimate model parameters. Experimental results show higher performance in frame-level chord recognition than the previous approaches.