20 accepted papers
Physical models of rigid bodies are used for sound synthesis in applications from virtual environments to music production. Traditional methods, such as modal synthesis, often rely on computationally expensive numerical solvers, while recent deep learning approaches are limited by post-processing of…
Modelling musical performers’ individual playing styles based on audio features is important for music education, music expression analysis and music generation. In violin performance, the perception of playing styles are mainly affected by the characteristic musical timbre, which is mostly determin…
An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbation…
Deep Learning (DL) is commonly used in music processing tasks such as Automatic Chord Recognition (ACR), for which Convolutional Neural Networks (CNNs) are popular tools. Compression of CNNs has become a research topic of interest, focused on post-pruning of learnt networks and development of less e…
Automatic Chord Recognition (ACR) seeks to extract chords from musical signals. Recently, deep neural network (DNN) approaches have become popular for this task, being employed for feature extraction and sequence modelling. Traditionally, the most important steps in ACR were extraction of chroma fea…
Recent research on piano transcription has focused primarily on note events. Very few studies have investigated pedalling techniques, which form an important aspect of expressive piano music performance. In this paper, we propose a novel method for piano sustain-pedal detection based on Convolutiona…
Onset detection is a fundamental task in musical signal processing, providing information for higher level applications. Different classes of onsets can be found in musical signals, determined as being hard, or soft, by the initial energy transfer. Most onset detectors are general purpose and attemp…
A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of-the-art approaches are supervised methods trained on large datasets, interest in non-data-driven approaches such as Kernel Additive Modelling (KAM) remains high du…
The expressive nature of the voice provides a powerful medium for communicating sonic ideas, motivating recent research on methods for query by vocalisation. Meanwhile, deep learning methods have demonstrated state-of-the-art results for matching vocal imitations to imitated sounds, yet little is kn…
We introduce a convolutional recurrent neural network (CRNN) for music tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local feature extraction and recurrent neural networks for temporal summarisation of the extracted features. We compare CRNN with three CNN structures that…
The task of chord recognition in music signals is often based upon pattern matching in chromagrams. Many variants of chroma exist and quality of chord recognition is related to the feature employed. Chroma Reduced Pitch (CRP) features are interesting in this context as they were designed to improve…
In live and studio recordings unexpected sound events often lead to interferences in the signal. For non-stationary interferences, sound source separation techniques can be used to reduce the interference level in the recording. In this context, we present a novel approach combining the strengths of…
Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with unc…
The estimation of rhythmic properties such as tempo, beat positions or metrical structure are central aspects of Music Information Retrieval (MIR) research. Meter inference algorithms are typically designed to track metrical structure in presence of mild deviations of the feature estimates over time…
Similar to non-negative matrix factorization (NMF), complex matrix factorization (CMF) can be used to decompose a given music recording into individual sound sources. In contrast to NMF, CMF models both the magnitude and phase of a source, which can improve the separation of overlapped partials. How…
Performance of Non-negative Matrix Factorisation (NMF) can be diminished when the underlying factors consist of elements that overlap in the matrix to be factorised. The use of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> sparsity may impro…
The design of systems for automatic audio feature extraction is a central aspect of the field of Music Information Retrieval. However, feature extraction systems often do not provide an indication of the reliability of the corresponding feature. Nevertheless, the provision of a reliability or confid…
Given a musical audio recording, the goal of music transcription is to determine a score-like representation of the piece underlying the recording. Most current transcription methods employ variants of non-negative matrix factorization (NMF), which often fails to robustly model instruments producing…
Non-negative Matrix Factorisation (NMF) is a commonly used tool in many musical signal processing tasks, including Automatic Music Transcription (AMT). However unsupervised NMF is seen to be problematic in this context, and harmonically constrained variants of NMF have been proposed. While useful, t…
This paper presents a new set of audio features to describe music content based on tempo cues. Tempogram, a mid-level representation of tempo information, is constructed to characterize tempo variation and local pulse in the audio signal. We introduce a collection of novel tempogram-based features i…