Download Analysis of Sound Field Distribution for Room Acoustics: From the Point of View of Hardware Implementation Analysis of sound field distribution is a data-intense and memory-intense application. To speed up calculation, an alternative solution is to implement the analysis algorithms by FPGA. This paper presents the related issues for FPGA based sound field analysis system from the point of view of hardware implementation. Compared with other algorithms, the OCTA-FDTD algorithm consumes 49 slices in FPGA, and the system updates 536.2 million elements per second. In system architecture, the system based on the parallel architecture benefits from fast computation since the sound pressures of all elements are obtained and updated at a clock cycle. But it consumes more hardware resources, and a small sound space is simulated by a FPGA chip. In contrast, the system based on the time-sharing architecture extends the simulated sound area by expense of computation speed since the sound pressures are calculated element by element.
Download Music Emotion Classification: Dataset Acquisition And Comparative Analysis In this paper we present an approach to emotion classification in audio music. The process is conducted with a dataset of 903 clips and mood labels, collected from Allmusic1 database, organized in five clusters similar to the dataset used in the MIREX2 Mood Classification Task. Three different audio frameworks – Marsyas, MIR Toolbox and Psysound, were used to extract several features. These audio features and annotations are used with supervised learning techniques to train and test various classifiers based on support vector machines. To access the importance of each feature several different combinations of features, obtained with feature selection algorithms or manually selected were tested. The performance of the solution was measured with 20 repetitions of 10-fold cross validation, achieving a F-measure of 47.2% with precision of 46.8% and recall of 47.6%.
Download Voice Features For Control: A Vocalist Dependent Method For Noise Measurement And Independent Signals Computation 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.