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

An energy-based generative sequence model for testing sensory theories of Western harmony

The relationship between sensory consonance and Western harmony is an important topic in music theory and psychology. We introduce new methods for analysing this relationship, and apply them to large corpora representing three prominent genres of Western music: classical, popular, and jazz music. These methods centre on a generative sequence model with an exponential-family energy-based form that predicts chord sequences from continuous features. We use this model to investigate one aspect of instantaneous consonance (harmonicity) and two aspects of sequential consonance (spectral distance and voice-leading distance). Applied to our three musical genres, the results generally support the relationship between sensory consonance and harmony, but lead us to question the high importance attributed to spectral distance in the psychological literature. We anticipate that our methods will provide a useful platform for future work linking music psychology to music theory

Filosax: A Dataset of Annotated Jazz Saxophone Recordings

The Filosax dataset is a large collection of specially commissioned recordings of jazz saxophonists playing with commercially available backing tracks. Five participants each recorded themselves playing the melody, interpreting a transcribed solo and improvising on 48 tracks, giving a total of around 24 hours of audio data. The solos are annotated both as individual note events with physical timing, and as sheet music with a metrical interpretation of the timing. In this paper, we outline the criteria used for choosing and sourcing the repertoire, the recording process and the semi-automatic transcription pipeline. We demonstrate the use of the dataset to analyse musical phenomena such as swing timing and dynamics of typical musical figures, as well as for training a source activity detection system and predicting expressive characteristics. Other potential applications include the modelling of jazz improvisation, performer identification, automatic music transcription, source separation and music generation

An efficient temporally-constrained probabilistic model for multiple-instrument music transcription

In this paper, an efficient, general-purpose model for multiple instrument polyphonic music transcription is proposed. The model is based on probabilistic latent component analysis and supports the use of sound state spectral templates, which represent the temporal evolution of each note (e.g. attack, sustain, decay). As input, a variable-Q transform (VQT) time-frequency representation is used. Computational efficiency is achieved by supporting the use of pre-extracted and pre-shifted sound state templates. Two variants are presented: without temporal constraints and with hidden Markov model-based constraints controlling the appearance of sound states. Experiments are performed on benchmark transcription datasets: MAPS, TRIOS, MIREX multiF0, and Bach10; results on multi-pitch detection and instrument assignment show that the proposed models outperform the state-of-the-art for multiple-instrument transcription and is more than 20 times faster compared to a previous sound state-based model. We finally show that a VQT representation can lead to improved multi-pitch detection performance compared with constant-Q representations

A study on LSTM networks for polyphonic music sequence modelling

Neural networks, and especially long short-term memory networks (LSTM), have become increasingly popular for sequence modelling, be it in text, speech, or music. In this paper, we investigate the predictive power of simple LSTM networks for polyphonic MIDI sequences, using an empirical approach. Such systems can then be used as a music language model which, combined with an acoustic model, can improve automatic music transcription (AMT) performance. As a first step, we experiment with synthetic MIDI data, and we compare the results obtained in various settings, throughout the training process. In particular, we compare the use of a fixed sample rate against a musically-relevant sample rate. We test this system both on synthetic and real MIDI data. Results are compared in terms of note prediction accuracy. We show that the higher the sample rate is, the better the prediction is, because self transitions are more frequent. We suggest that for AMT, a musically-relevant sample rate is crucial in order to model note transitions, beyond a simple smoothing effect

Real-time Percussive Technique Recognition and Embedding Learning for the Acoustic Guitar

Real-time music information retrieval (RT-MIR) has much potential to augment the capabilities of traditional acoustic instruments. We develop RT-MIR techniques aimed at augmenting percussive fingerstyle, which blends acoustic guitar playing with guitar body percussion. We formulate several design objectives for RT-MIR systems for augmented instrument performance: (i) causal constraint, (ii) perceptually negligible action-to-sound latency, (iii) control intimacy support, (iv) synthesis control support. We present and evaluate real-time guitar body percussion recognition and embedding learning techniques based on convolutional neural networks (CNNs) and CNNs jointly trained with variational autoencoders (VAEs). We introduce a taxonomy of guitar body percussion based on hand part and location. We follow a cross-dataset evaluation approach by collecting three datasets labelled according to the taxonomy. The embedding quality of the models is assessed using KL-Divergence across distributions corresponding to different taxonomic classes. Results indicate that the networks are strong classifiers especially in a simplified 2-class recognition task, and the VAEs yield improved class separation compared to CNNs as evidenced by increased KL-Divergence across distributions. We argue that the VAE embedding quality could support control intimacy and rich interaction when the latent space's parameters are used to control an external synthesis engine. Further design challenges around generalisation to different datasets have been identified