Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes

In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for sound event detection and classification using weakly labeled audio data. Our model trains efficiently from audios of variable lengths; hence, it is well suited for transfer learning. We then propose methods to learn representations using this model which can be effectively used for solving the target task. We study both transductive and inductive transfer learning tasks, showing the effectiveness of our methods for both domain and task adaptation. We show that the learned representations using the proposed CNN model generalizes well enough to reach human level accuracy on ESC-50 sound events dataset and set state of art results on this dataset. We further use them for acoustic scene classification task and once again show that our proposed approaches suit well for this task as well. We also show that our methods are helpful in capturing semantic meanings and relations as well. Moreover, in this process we also set state-of-art results on Audioset dataset, relying on balanced training set.Comment: ICASSP 201

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

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Music signal processing for automatic extraction of harmonic and rhythmic information

This thesis is concerned with the problem of automatic extraction of harmonic and rhythmic information from music audio signals using statistical framework and advanced signal processing methods. Among different research directions, automatic extraction of chords and key has always been of a great interest to Music Information Retrieval (MIR) community. Chord progressions and key information can serve as a robust mid-level representation for a variety of MIR tasks. We propose statistical approaches to automatic extraction of chord progressions using Hidden Markov Models (HMM) based framework. General ideas we rely on have already proved to be effective in speech recognition. We propose novel probabilistic approaches that include acoustic modeling layer and language modeling layer. We investigate the usage of standard N-grams and Factored Language Models (FLM) for automatic chord recognition. Another central topic of this work is the feature extraction techniques. We develop a set of new features that belong to chroma family. A set of novel chroma features that is based on the application of Pseudo-Quadrature Mirror Filter (PQMF) bank is introduced. We show the advantage of using Time-Frequency Reassignment (TFR) technique to derive better acoustic features. Tempo estimation and beat structure extraction are amongst the most challenging tasks in MIR community. We develop a novel method for beat/downbeat estimation from audio. It is based on the same statistical approach that consists of two hierarchical levels: acoustic modeling and beat sequence modeling. We propose the definition of a very specific beat duration model that exploits an HMM structure without self-transitions. A new feature set that utilizes the advantages of harmonic-impulsive component separation technique is introduced. The proposed methods are compared to numerous state-of-the-art approaches by participation in the MIREX competition, which is the best impartial assessment of MIR systems nowadays

Use of Hidden Markov Models and Factored Language Models for Automatic Chord Recognition

This paper focuses on automatic extraction of acoustic chord sequences from a musical piece. Standard and factored language models are analyzed in terms of applicability to the chord recognition task. Pitch class profile vectors that represent harmonic information are extracted from the given audio signal. The resulting chord sequence is obtained by running a Viterbi decoder on trained hidden Markov models and subsequent lattice rescoring, applying the language model weight. We performed several experiments using the proposed technique. Results obtained on 175 manually-labeled songs provided an increase in accuracy of about 2%

Phase-change based tuning for automatic chord recognition

This paper focuses on automatic extraction of acoustic chord sequences from a piece of music. Firstly, the evaluation of a set of different windowing methods for Discrete Fourier Transform is investigated in terms of their efficiency. Then, a new tuning solution is introduced, based on a method developed in the past for phase vocoder. Pitch class profile vectors, that represent harmonic information, are extracted from the given audio signal. The resulting chord sequence is obtained by running a Viterbi decoder on trained hidden Markov models. We performed several experiments using the proposed technique. Results obtained on 175 manually-labeled songs provided an accuracy that is comparable to the state of the art. 1