Chord Segmentation and Recognition using EM-Trained Hidden Markov Models

Automatic extraction of content description from commercial audio recordings has a number of important applications, from indexing and retrieval through to novel musicological analyses based on very large corpora of recorded performances. Chord sequences are a description that captures much of the character of a piece in a compact form and using a modest lexicon. Chords also have the attractive property that a piece of music can (mostly) be segmented into time intervals that consist of a single chord, much as recorded speech can (mostly) be segmented into time intervals that correspond to specific words. In this work, we build a system for automatic chord transcription using speech recognition tools. For features we use "pitch class profile" vectors to emphasize the tonal content of the signal, and we show that these features far outperform cepstral coefficients for our task. Sequence recognition is accomplished with hidden Markov models (HMMs) directly analogous to subword models in a speech recognizer, and trained by the same Expectation-Maximization (EM) algorithm. Crucially, this allows us to use as input only the chord sequences for our training examples, without requiring the precise timings of the chord changes— which are determined automatically during training. Our results on a small set of 20 early Beatles songs show frame-level accuracy of around 75% on a forced-alignment task

A Unified System for Chord Transcription and Key Extraction Using Hidden Markov Models.

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A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.Comment: In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietro sul Mare, Ital

Integrating musicological knowledge into a probabilistic framework for chord and key extraction

In this contribution a formerly developed probabilistic framework for the simultaneous detection of chords and keys in polyphonic audio is further extended and validated. The system behaviour is controlled by a small set of carefully defined free parameters. This has permitted us to conduct an experimental study which sheds a new light on the importance of musicological knowledge in the context of chord extraction. Some of the obtained results are at least surprising and, to our knowledge, never reported as such before

Modeling musicological information as trigrams in a system for simultaneous chord and local key extraction

In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord and local key extraction system. By enlarging the context of the musicological model, we hoped to achieve a higher accuracy that could justify the associated higher complexity and computational load of the search for the optimal solution. Experiments on multiple data sets have demonstrated that the trigram model has indeed a larger predictive power (a lower perplexity). This raised predictive power resulted in an improvement in the key extraction capabilities, but no improvement in chord extraction when compared to a system with a bigram musicological model