Paper Archive

A supervised learning approach to ambience extraction from onechannel audio signals is presented. The extracted ambient signals are applied for the blind upmixing of musical audio recordings to surround sound formats. The input signal is processed by means of short-term spectral attenuation. The spectral weights are computed using a low-level feature extraction process and a neural network regression method. The multi-channel audio signal is generated by feeding the computed ambient signal into the rear channels of a surround sound system.

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The ability of a sound synthesizer to provide realistic sounds depends to a great extent on the availability of expressive controls. One of the most important expressive features a user of the synthesizer would desire to have control of, is timbre. Timbre is a complex concept related to many musical indications in a score such as dynamics, accents, hand position, string played, or even indications referring timbre itself. Musical indications are in turn related to low level performance controls such as bow velocity or bow force. With the help of a data acquisition system able to record sound synchronized to performance controls and aligned to the performed score and by means of statistical analysis, we are able to model the interrelations among sound (timbre), controls and musical score indications. In this paper we present a procedure for score-controlled timbre transformations of violin sounds within a sample based synthesizer. Given a sound sample and its trajectory of performance controls: 1) a transformation of the controls trajectory is carried out according to the score indications, 2) a new timbre corresponding to the transformed trajectory is predicted by means of a timbre model that relates timbre with performance controls and 3) the timbre of the original sound is transformed by applying a timevarying filter calculated frame by frame as the difference of the original and predicted envelopes.

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This paper presents a new approach to the visualization of monophonic audio files that simultaneously illustrates general audio properties and the component sounds that comprise a given input file. This approach represents sound clip sequences using archetypal images which are subjected to image processing filters driven by audio characteristics such as power, pitch and signalto-noise ratio. Where the audio is comprised of a single sound it is represented by a single image that has been subjected to filtering. Heterogeneous audio files are represented as a seamless image mosaic along a time axis where each component image in the mosaic maps directly to a discovered component sound. To support this, in a given audio file, the system separates individual sounds and reveals the overlapping period between sound clips. Compared with existing visualization methods such as oscilloscopes and spectrograms, this approach yields more accessible illustrations of audio files, which are suitable for casual and nonexpert users. We propose that this method could be used as an efficient means of scanning audio database queries and navigating audio databases through browsing, since the user can visually scan the file contents and audio properties simultaneously.

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This paper introduces an automatic classification system for the identification of individual classical guitars by single notes played on these guitars. The classification is performed by Support Vector Machines (SVM) that have been trained with the features of the single notes. The features used for classification were the time series of the partial tones, the time series of the MFCCs (Mel Frequency Cepstral Coefficients), and the “nontonal” contributions to the spectrum. The influences of these features on the classification success are reported. With this system, 80% of the sounds recorded with three different guitars were classified correctly. A supplementary classification experiment was carried out with human listeners resulting in a rate of 65% of correct classifications.

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This study explores the potential of utilising certain prosodic qualities of function-specific vocal expressions in order to design effective non-speech user interface sounds. In an empirical setting, utterances with four context-situated communicative functions were produced by 20 participants. Time series of fundamental frequency (F0 ) and intensity were extracted from the utterances and analysed statistically. The results show that individual communicative functions have distinct prosodic characteristics that can be statistically modelled. By using the model, certain function-specific prosodic cues can be identified and, in turn, imitated in the design of communicative interface sounds for the corresponding communicative functions in human-computer interaction.

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Techniques for the generation of bandlimited signals for application to digital implementations of subtractive synthesis have been researched by a number of authors. This paper hopes to contribute to the variety of approaches by proposing a technique based on Frequency Modulation (FM) synthesis. This paper presents and explains the equations required for bandlimited pulse generation using modified FM synthesis. It then investigates the relationships between the modulation index and the quality of the reproduction in terms of authenticity and aliasing for a sawtooth wave. To determine the performance of this technique in comparison to others two sets of simulation results are offered: the first computes the relative power of the non-harmonic components, and the second uses the Perceptual Evaluation of Audio Quality (PEAQ) algorithm. It is shown that this technique compares well with the alternatives. The paper concludes with suggestions for the direction of future improvements to the method.

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In many applications of sound transformation, such as sound design, mixing, mastering, and composition the user interactively searches for appropriate parameters. However, automatic applications of sound transformation, such as mosaicing, may require choosing parameters without user intervention. When the target can be specified by its synthesis context, or by example (from features of the example), “adaptive effects” can provide such control. But there exist few general strategies for building adaptive effects from arbitrary sets of transformations and descriptor targets. In this study, we decouple the usually direct link between analysis and transformation in adaptive effects, attempting to include more diverse transformations and descriptors in adaptive transformation, if at the cost of additional complexity or difficulty. We build an analytic model of a deliberately simple transformation-descriptor (TD) domain, and show some preliminary results.

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Many speech source separation approaches are based on the assumption of orthogonality of speech sources in the time-frequency domain. The target speech source is demixed from the mixture by applying the ideal binary mask to the mixture. The time-frequency orthogonality of speech sources is investigated in detail only for anechoic and artificially mixed speech mixtures. This paper evaluates how the orthogonality of speech sources decreases when using a realistic reverberant humanoid recording setup and indicates strategies to enhance the separation capabilities of algorithms based on ideal binary masks under these conditions. It is shown that the SIR of the target source demixed from the mixture using the ideal binary mask decreases by approximately 3 dB for reverberation times of T60 = 0.6 s opposed to the anechoic scenario. For humanoid setups, the spatial distribution of the sources and the choice of the correct ear channel introduces differences in the SIR of further 3 dB, which leads to specific strategies to choose the best channel for demixing.

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