Download DHM and FDTD based Hardware Sound Field Simulation Acceleration Sound field simulation is widely used for acoustic design; however, this simulation needs many computational resources. On the other hand, FPGA becomes major for acceleration. To take advantage of hardware acceleration by FPGA, hardware oriented algorithm which consumes small number of gates and memory is necessary. This paper addresses hardware acceleration of sound field simulation using FPGA. Improved Digital Huygens Model (DHM) for hardware is implemented and speed up ratio is examined. For 2D simulation, the implemented accelerator is 1,170 times faster than software simulation. For 3D simulation, it is shown that FDTD based method is suitable for hardware implementation and required hardware resource are estimated.
Download State of the Art in Sound Texture Synthesis The synthesis of sound textures, such as rain, wind, or crowds, is an important application for cinema, multimedia creation, games and installations. However, despite the clearly defined requirments of naturalness and flexibility, no automatic method has yet found widespread use. After clarifying the definition, terminology, and usages of sound texture synthesis, we will give an overview of the many existing methods and approaches, and the few available software implementations, and classify them by the synthesis model they are based on, such as subtractive or additive synthesis, granular synthesis, corpus-based concatenative synthesis, wavelets, or physical modeling. Additionally, an overview is given over analysis methods used for sound texture synthesis, such as segmentation, statistical modeling, timbral analysis, and modeling of transitions. 2
Download A Single-Azimuth Pinna-Related Transfer Function Database Pinna-Related Transfer Functions (PRTFs) reflect the modifications undergone by an acoustic signal as it interacts with the listener’s outer ear. These can be seen as the pinna contribution to the Head-Related Transfer Function (HRTF). This paper describes a database of PRTFs collected from measurements performed at the Department of Signal Processing and Acoustics, Aalto University. Median-plane PRTFs at 61 different elevation angles from 25 subjects are included. Such data collection falls into a broader project in which evidence of the correspondence between PRTF features and anthropometry is being investigated.
Download The Image-Source Reverberation Model in an N-Dimensional Space The image method is generalized to geometries with an arbitrary number of spatial dimensions. n-dimensional (n-D) acoustics is discussed, and an algorithm for n-D room impulse response calculations is presented. Synthesized room impulse responses (RIRs) from n-D rooms are presented. RIR characteristics are discussed, and computational considerations are examined.
Download Combining classifications based on local and global features: application to singer identification In this paper we investigate the problem of singer identification on acapella recordings of isolated notes. Most of studies on singer identification describe the content of signals of singing voice with features related to the timbre (such as MFCC or LPC). These features aim to describe the behavior of frequencies at a given instant of time (local features). In this paper, we propose to describe sung tone with the temporal variations of the fundamental frequency (and its harmonics) of the note. The periodic and continuous variations of the frequency trajectories are analyzed on the whole note and the features obtained reflect expressive and intonative elements of singing such as vibrato, tremolo and portamento. The experiments, conducted on two distinct data-sets (lyric and pop-rock singers), prove that the new set of features capture a part of the singer identity. However, these features are less accurate than timbre-based features. We propose to increase the recognition rate of singer identification by combining information conveyed by local and global description of notes. The proposed method, that shows good results, can be adapted for classification problem involving a large number of classes, or to combine classifications with different levels of performance.