A Corpus-based Study Of Rhythm Patterns

We present a corpus-based study of musical rhythm, based on a collection of 4.8 million bar-length drum patterns extracted from 48,176 pieces of symbolic music. Approaches to the analysis of rhythm in music information retrieval to date have focussed on low-level features for retrieval or on the detection of tempo, beats and drums in audio recordings. Musicological approaches are usually concerned with the description or implementation of manmade music theories. In this paper, we present a quantitative bottom-up approach to the study of rhythm that relies upon well-understood statistical methods from natural language processing. We adapt these methods to our corpus of music, based on the realisation that—unlike words—barlength drum patterns can be systematically decomposed into sub-patterns both in time and by instrument. We show that, in some respects, our rhythm corpus behaves like natural language corpora, particularly in the sparsity of vocabulary. The same methods that detect word collocations allow us to quantify and rank idiomatic combinations of drum patterns. In other respects, our corpus has properties absent from language corpora, in particular, the high amount of repetition and strong mutual information rates between drum instruments. Our findings may be of direct interest to musicians and musicologists, and can inform the design of ground truth corpora and computational models of musical rhythm. 1

PYIN: A FUNDAMENTAL FREQUENCY ESTIMATOR USING PROBABILISTIC THRESHOLD DISTRIBUTIONS

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Sequential Complexity as a Descriptor for Musical Similarity

We propose string compressibility as a descriptor of temporal structure in audio, for the purpose of determining musical similarity. Our descriptors are based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks. We base our evaluation on a dataset of 15500 track excerpts of Western popular music, for which we obtain 7800 web-sourced pairwise similarity ratings. To assess the agreement among similarity ratings, we perform an evaluation under controlled conditions, obtaining a rank correlation of 0.33 between intersected sets of ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio

Timbre-invariant Audio Features for Style Analysis of Classical Music

Copyright: (c) 2014 Christof Weiß et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

A COMPARISON OF EXTENDED SOURCE-FILTER MODELS FOR MUSICAL SIGNAL RECONSTRUCTION

China Scholarship Council (CSC)/ Queen Mary Joint PhD scholarship; Royal Academy of Engineering Research Fellowshi

The Audio Degradation Toolbox and its Application to Robustness Evaluation

We introduce the Audio Degradation Toolbox (ADT) for the controlled degradation of audio signals, and propose its usage as a means of evaluating and comparing the robustness of audio processing algorithms. Music recordings encountered in practical applications are subject to varied, sometimes unpredictable degradation. For example, audio is degraded by low-quality microphones, noisy recording environments, MP3 compression, dynamic compression in broadcasting or vinyl decay. In spite of this, no standard software for the degradation of audio exists, and music processing methods are usually evaluated against clean data. The ADT fills this gap by providing Matlab scripts that emulate a wide range of degradation types. We describe 14 degradation units, and how they can be chained to create more complex, `real-world' degradations. The ADT also provides functionality to adjust existing ground-truth, correcting for temporal distortions introduced by degradation. Using four different music informatics tasks, we show that performance strongly depends on the combination of method and degradation applied. We demonstrate that specific degradations can reduce or even reverse the performance difference between two competing methods. ADT source code, sounds, impulse responses and definitions are freely available for download

Drum Transcription via Classification of Bar-level Rhythmic Patterns

acceptedMatthias Mauch is supported by a Royal Academy of Engineering Research Fellowshi

High precision frequency estimation for harpsichord tuning classification

We present a novel music signal processing task of classifying the tuning of a harpsichord from audio recordings of standard musical works. We report the results of a classification experiment involving six different temperaments, using real harpsichord recordings as well as synthesised audio data. We introduce the concept of conservative transcription, and show that existing high-precision pitch estimation techniques are sufficient for our task if combined with conservative transcription. In particular, using the CQIFFT algorithm with conservative transcription and removal of short duration notes, we are able to distinguish between 6 different temperaments of harpsichord recordings with 96% accuracy (100% for synthetic data)