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 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

Analysis and classification of phonation modes in singing

Phonation mode is an expressive aspect of the singing voice and can be described using the four categories neutral, breathy, pressed and flow. Previous attempts at automatically classifying the phonation mode on a dataset containing vowels sung by a female professional have been lacking in accuracy or have not sufficiently investigated the characteristic features of the different phonation modes which enable successful classification. In this paper, we extract a large range of features from this dataset, including specialised descriptors of pressedness and breathiness, to analyse their explanatory power and robustness against changes of pitch and vowel. We train and optimise a feed-forward neural network (NN) with one hidden layer on all features using cross validation to achieve a mean F-measure above 0.85 and an improved performance compared to previous work. Applying feature selection based on mutual information and retaining the nine highest ranked features as input to a NN results in a mean F-measure of 0.78, demonstrating the suitability of these features to discriminate between phonation modes. Training and pruning a decision tree yields a simple rule set based only on cepstral peak prominence (CPP), temporal flatness and average energy that correctly categorises 78% of the recordings

Learning a feature space for similarity in world music

In this study we investigate computational methods for assessing music similarity in world music styles. We use state-of-the-art audio features to describe musical content in world music recordings. Our music collection is a subset of the Smithsonian Folkways Recordings with audio examples from 31 countries from around the world. Using supervised and unsupervised dimensionality reduction techniques we learn feature representations for music similarity. We evaluate how well music styles separate in this learned space with a classification experiment. We obtained moderate performance classifying the recordings by country. Analysis of misclassifications revealed cases of geographical or cultural proximity. We further evaluate the learned space by detecting outliers, i.e. identifying recordings that stand out in the collection. We use a data mining technique based on Mahalanobis distances to detect outliers and perform a listening experiment in the ‘odd one out’ style to evaluate our findings. We are able to detect, amongst others, recordings of non-musical content as outliers as well as music with distinct timbral and harmonic content. The listening experiment reveals moderate agreement between subjects’ ratings and our outlier estimation

Report on the Standardization Project ``Formal Methods in Conformance Testing''

This paper presents the latest developments in the “Formal Methods in Conformance Testing” (FMCT) project of ISO and ITU–T. The project has been initiated to study the role of formal description techniques in the conformance testing process. The goal is to develop a standard that defines the meaning of conformance in the context of formal description techniques. We give an account of the current status of FMCT in the standardization process as well as an overview of the technical status of the proposed standard. Moreover, we indicate some of its strong and weak points, and we give some directions for future work on FMCT