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Listeners' emotional engagement with performances of a Scriabin étude : an explorative case study
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56757.pdf (Publisher’s version ) (Closed access)In an explorative study, the variation in listeners' judgments of the emotionality of three performances of a Scriabinétude was investigated. Three performances of the Scriabinétude were recorded. Video and/or audio recordings were presented to 24 listeners who segmented the music in short phrases and indicated their emotional engagement with the music using a slider. The relation between the performance data and the listeners' responses was analysed as well as the effects of musical training, medium and musical structure. The analyses were done using multiple regression analyses and decision trees. The results confirmed the hypothesized influence of the performer's interpretation, the listener's background and the global phrase structure, though not always in the expected way. In particular the dynamics of the performance correlated with listeners' judgments of emotionality, while tempo correlated more strongly with the indications of phrase structure. This was more clearly the case for nonmusicians than for musicians. The movements of the pianist were related to the dynamics of the performance and seemed to aid the communication of emotional intensity.30 p
A New Approach to HRTF Audio Spatialization
No full textThe article presents a new audio spatialization algorithm for spatialization methods based on the headrelated transfer function (HRTF). The algorithm uses a mathematical model of the HRTF to make the spatialization process simple and fast. The article thoroughly describes this algorithm and provides a comparison of the algorithm to the traditional block convolution filtering approach
A New Approach to HRTF Audio Spatialization
No full textThe article presents a new audio spatialization algorithm for spatialization methods based on the headrelated transfer function (HRTF). The algorithm uses a mathematical model of the HRTF to make the spatialization process simple and fast. The article thoroughly describes this algorithm and provides a comparison of the algorithm to the traditional block convolution filtering approach. 1. Introduction Audio spatialization can be achieved in a variety of ways. The most commonly used method is based on the head-related transfer function (HRTF), which represents the changes in sound as it travels from its source towards the listener's middle ear. The core of HRTF spatialization usually consists of a block convolution algorithm that filters the incoming audio stream with the HRTF and thereby calculates the spatialized result. This approach has two drawbacks: . its computational demands are very high, even when very fast block convolution algorithms are used; . immediate response to chang..
A Mid-level Melody-based Representation for Calculating Audio Similarity.
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A Connectionist Model of Finding Partial Groups in Music Recordings with Application to Music Transcription
No full textIn this paper, we present a technique for tracking groups of partials in musical signals, based on networks of adaptive oscillators. We show how synchronization of adaptive oscillators can be utilized to detect periodic patterns in outputs of a human auditory model and thus track stable frequency components (partials) in musical signals. We present the integration of the partial tracking model into a connectionist system for transcription of polyphonic piano music. We provide a short overview of our transcription system and present its performance on transcriptions of several real piano recordings
Probabilistic Segmentation and Labeling of Ethnomusicological Field Recordings.
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Gaussian Mixture Models for Extraction of Melodic Lines from Audio Recordings
No full textThe presented study deals with extraction of melodic line(s) from polyphonic audio recordings. We base our work on the use of expectation maximization algorithm, which is employed in a two-step procedure that finds melodic lines in audio signals. In the first step, EM is used to find regions in the signal with strong and stable pitch (melodic fragments). In the second step, these fragments are grouped into clusters according to their properties (pitch, loudness...). The obtained clusters represent distinct melodic lines. Gaussian Mixture Models, trained with EM are used for clustering. The paper presents the entire process in more detail and gives some initial results
Non-negative Matrix Factorization With Selective Sparsity Constraints for Transcription of Bell Chiming Recordings
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A Connectionist Model of Finding Partial Groups in Music Recordings with Application to Music Transcription
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