Estimating timing and channel distortion across related signals

We consider the situation where there are multiple audio sig-nals whose relationship is of interest. If these signals have been dif-ferently captured, the otherwise similar signals may be distorted by fixed filtering and/or unsynchronized timebases. Examples include recordings of signals before and after radio transmission and differ-ent versions of musical mixes obtained from CDs and vinyl LPs. We present techniques for estimating and correcting timing and channel differences across related signals. Our approach is evaluated in the context of artificially manipulated speech utterances and two source separation tasks

GPU Acceleration of Melody Accurate Matching in Query-by-Humming

With the increasing scale of the melody database, the query-by-humming system faces the trade-offs between response speed and retrieval accuracy. Melody accurate matching is the key factor to restrict the response speed. In this paper, we present a GPU acceleration method for melody accurate matching, in order to improve the response speed without reducing retrieval accuracy. The method develops two parallel strategies (intra-task parallelism and inter-task parallelism) to obtain accelerated effects. The efficiency of our method is validated through extensive experiments. Evaluation results show that our single GPU implementation achieves 20x to 40x speedup ratio, when compared to a typical general purpose CPU's execution time

On the Use of Speech Recognition Techniques to Identify Bird Species

Abstract Wild bird watching has become a popular leisure activity in recent years. Very often, people can see birds or hear their sounds, but have no idea what kind of bird species they are seeing. To help people learn to identify bird species from their sounds, we apply speech recognition techniques to build an automatic bird sound identification system. In this system, two acoustic cues are used for analysis, timbre and pitch. In the timbre-based analysis, Mel-Frequency Cepstral Coefficients (MFCCs) are used to characterize the bird sound. Then, we use Gaussian Mixture Models to represent the MFCCs as a set of parameters. In the pitch-based analysis, we convert bird sounds from their waveform representations into a sequence of MIDI notes. Then, Bigram models are used to capture the dynamic change information of the notes. We chose the top ten common bird species in the Taipei urban area to examine our system. Experiments conducted using audio data collected from commercial CDs and websites show that the timbre-based, pitch-based, and the combination thereof systems achieve 71.1%, 72.1%, and 75.0% accuracy of bird sound identification, respectively

A Query-by-Singing System for Retrieving Karaoke Music

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