MID-LEVEL FEATURES FOR AUDIO CHORD RECOGNITION USING A DEEP NEURAL NETWORK // Ученые записки КФУ. Физико-математические науки 2013 том155 N4

Глубокие нейронные сети, состоящие из нескольких предварительно обученных слоёв, успешно применяются в задачах, связанных с обработкой звука. В данной работе предложены и рассмотрены применительно к задаче распознавания аккордов некоторые конфигурации глубоких нейронных сетей (в том числе рекуррентных), допускающие предварительное послойное обучение при помощи многослойных очищающих автоассоциаторов. Рассмотренные нейронные сети позволяют преобразовывать спектрограмму звукозаписи в последовательность векторов признаков, по которой затем определяются звучащие аккорды. Качество распознавания аккордов, достигнутое с использованием описанных признаков, сравнивается с качеством распознавания аккордов, достигнутым при помощи часто используемых хроматических признаков, при вычислении которых не используются методы машинного обучения

Harmonic Change Detection from Musical Audio

In this dissertation, we advance an enhanced method for computing Harte et al.’s [31] Harmonic Change Detection Function (HCDF). HCDF aims to detect harmonic transitions in musical audio signals. HCDF is crucial both for the chord recognition in Music Information Retrieval (MIR) and a wide range of creative applications. In light of recent advances in harmonic description and transformation, we depart from the original architecture of Harte et al.’s HCDF, to revisit each one of its component blocks, which are evaluated using an exhaustive grid search aimed to identify optimal parameters across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes, by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at > 99%, our method improves precision by 6.28%. Aiming to leverage novel strategies for real-time harmonic-content audio processing, the optimized HCDF is made available for Javascript and the MAX and Pure Data multimedia programming environments. Moreover, all the data as well as the Python code used to generate them, are made available.<br /

EVALUATING AUTOMATICALLY ESTIMATED CHORD SEQUENCES

In this paper, we perform an in-depth evaluation of a large number of algorithms for chord estimation that have been submitted to the MIREX competitions in 2010, 2011 and 2012. Therefore we first present a rigorous scheme to describe evaluation methods in a sound, unambiguous way that extends previous work specifically to take into account the large variance in chord estimation vocabularies and to perform evaluations on select sets of chords. Then we take a look at the evaluation metrics used so far and propose some alternative ones. Finally, we use these different methods to get a deeper insight into the strengths of each of the competing algorithms and show that the choice of evaluation measure greatly influences the ranking. Index Terms — music information retrieval, chord estimation, evaluation procedure, large scale evaluation 1