Download Identification of individual guitar sounds by support vector machines 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.
Download A FPGA‐based Adaptive Noise Cancelling System A FPGA-based system suitable for augmented reality audio applications is presented. The sample application described here is adaptive noise cancellation (ANC). The system consists of a Spartan -3 FPGA XC3S400 board connected to a Philips Stereo-AudioCodec UCB 1400. The algorithms for the FIR filtering and for the adaption of the filter coefficients according to the Widrow-Hoff LMS algorithm are implemented on the FPGA board. Measurement results obtained with a dummy head measuring system are reported, and a detailed analysis of system performance and possible system improvements is given.