Paper Archive

Onset detectors are used to recognize the beginning of musical events in audio signals. Manual parameter tuning for onset detectors is a time consuming task, while existing automated approaches often maximize only a single performance metric. These automated approaches cannot be used to optimize detector algorithms for complex scenarios, such as real-time onset detection where an optimization process must consider both detection accuracy and latency. For this reason, a flexible optimization algorithm should account for more than one performance metric in a multiobjective manner. This paper presents a generalized procedure for automated optimization of parametric onset detectors. Our procedure employs a bio-inspired evolutionary computation algorithm to replace manual parameter tuning, followed by the computation of the Pareto frontier for multi-objective optimization. The proposed approach was evaluated on all the onset detection methods of the Aubio library, using a dataset of monophonic acoustic guitar recordings. Results show that the proposed solution is effective in reducing the human effort required in the optimization process: it replaced more than two days of manual parameter tuning with 13 hours and 34 minutes of automated computation. Moreover, the resulting performance was comparable to that obtained by manual optimization.

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In this paper we evaluate some of the alternative methods commonly applied in the first stages of the signal processing chain of automatic melody extraction systems. Namely, the first two stages are studied – the extraction of sinusoidal components and the computation of a time-pitch salience function, with the goal of determining the benefits and caveats of each approach under the specific context of predominant melody estimation. The approaches are evaluated on a data-set of polyphonic music containing several musical genres with different singing/playing styles, using metrics specifically designed for measuring the usefulness of each step for melody extraction. The results suggest that equal loudness filtering and frequency/amplitude correction methods provide significant improvements, whilst using a multi-resolution spectral transform results in only a marginal improvement compared to the standard STFT. The effect of key parameters in the computation of the salience function is also studied and discussed.

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Slow motion videos are frequently featured during broadcast of sports events. However, these videos do not feature any audio channel, apart from the live ambiance and comments from sports presenters. Standard audio time-scaling methods were not developed with such noisy signal in mind and they do not always permit to obtain an acceptable acoustic quality. In this work, we present a new approach that creates high-quality time-stretched version of sport audio recordings while preserving all their transient events.

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Soundscape research is concerned with the study and understanding of our relationship with our surrounding acoustic environments and the sonic elements that they are comprised of. Whilst much of this research has focussed on sound alone, any practical application of soundscape methodologies should consider the interaction between aural and visual environmental features: an interaction known as cross-modal perception. This presents an avenue for soundscape research exploring how an environment’s visual features can affect an individual’s experience of the soundscape of that same environment. This paper presents the results of two listening tests1 : one a preliminary test making use of static stereo UHJ renderings of first-order-ambisonic (FOA) soundscape recordings and static panoramic images; the other using YouTube as a platform to present dynamic binaural renderings of the same FOA recordings alongside full motion spherical video. The stimuli for these tests were recorded at several locations around the north of England including rural, urban, and suburban environments exhibiting soundscapes comprised of many natural, human, and mechanical sounds. The purpose of these tests was to investigate how the presence of visual stimuli can alter soundscape perception and categorisation. This was done by presenting test subjects with each soundscape alone and then with visual accompaniment, and then comparing collected subjective evaluation data. Results indicate that the presence of certain visual features can alter the emotional state evoked by exposure to a soundscape, for example, where the presence of ‘green infrastructure’ (parks, trees, and foliage) results in a less agitating experience of a soundscape containing high levels of environmental noise. This research represents an important initial step toward the integration of virtual reality technologies into soundscape research, and the use of suitable tools to perform subjective evaluation of audiovisual stimuli. Future research will consider how these methodologies can be implemented in real-world applications.

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Information about the human spoken and singing voice is conveyed through the articulations of the individual’s vocal folds and vocal tract. The signal receiver, either human or machine, works at different levels of abstraction to extract and interpret only the relevant context specific information needed. Traditionally in the field of human machine interaction, the human voice is used to drive and control events that are discrete in terms of time and value. We propose to use the voice as a source of realvalued and time-continuous control signals that can be employed to interact with any multidimensional human-controllable device in real-time. The isolation of noise sources and the independence of the control dimensions play a central role. Their dependency on individual voice represents an additional challenge. In this paper we introduce a method to compute case specific independent signals from the vocal sound, together with an individual study of features computation and selection for noise rejection.

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