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

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An end-to-end machine learning system for harmonic analysis of music

We present a new system for simultaneous estimation of keys, chords, and bass notes from music audio. It makes use of a novel chromagram representation of audio that takes perception of loudness into account. Furthermore, it is fully based on machine learning (instead of expert knowledge), such that it is potentially applicable to a wider range of genres as long as training data is available. As compared to other models, the proposed system is fast and memory efficient, while achieving state-of-the-art performance.Comment: MIREX report and preparation of Journal submissio

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

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

Reassigned spectrum-based feature extraction for GMM-based automatic chord recognition

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Improving the key extraction performance of a simultaneous local key and chord estimation system

In this paper, significant improvements of a previously developed key and chord extraction system are proposed. The major improvement is the introduction of a separate acoustic model, designed to verify local key hypotheses. The conducted experimental evaluation shows that the presented system improves the state of the art in local key estimation. Our experimental study further demonstrates that the chord estimation performance is already quite robust, whereas the key estimation performance still happens to be sensitive to a number of factors. In particular, we present figures that illustrate the significant impact of the embedded musicological model and the duration of the processed excerpt on the key estimation accuracy

Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing

Automatic chord transcription from audio using computational models of musical context

PhDThis thesis is concerned with the automatic transcription of chords from audio, with an emphasis on modern popular music. Musical context such as the key and the structural segmentation aid the interpretation of chords in human beings. In this thesis we propose computational models that integrate such musical context into the automatic chord estimation process. We present a novel dynamic Bayesian network (DBN) which integrates models of metric position, key, chord, bass note and two beat-synchronous audio features (bass and treble chroma) into a single high-level musical context model. We simultaneously infer the most probable sequence of metric positions, keys, chords and bass notes via Viterbi inference. Several experiments with real world data show that adding context parameters results in a significant increase in chord recognition accuracy and faithfulness of chord segmentation. The proposed, most complex method transcribes chords with a state-of-the-art accuracy of 73% on the song collection used for the 2009 MIREX Chord Detection tasks. This method is used as a baseline method for two further enhancements. Firstly, we aim to improve chord confusion behaviour by modifying the audio front end processing. We compare the effect of learning chord profiles as Gaussian mixtures to the effect of using chromagrams generated from an approximate pitch transcription method. We show that using chromagrams from approximate transcription results in the most substantial increase in accuracy. The best method achieves 79% accuracy and significantly outperforms the state of the art. Secondly, we propose a method by which chromagram information is shared between repeated structural segments (such as verses) in a song. This can be done fully automatically using a novel structural segmentation algorithm tailored to this task. We show that the technique leads to a significant increase in accuracy and readability. The segmentation algorithm itself also obtains state-of-the-art results. A method that combines both of the above enhancements reaches an accuracy of 81%, a statistically significant improvement over the best result (74%) in the 2009 MIREX Chord Detection tasks.Engineering and Physical Research Council U