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The research efforts recently devoted to the theoretical treatment of time-varying filter bank structures have created an adequate environment for the development of adaptive analysis tools, useful in many signal processing problems. In this paper we present a complete adaptive analysis system suitable for the compression of audio signals; we would like to remark that our goal is not to develop...

A key problem for telecommunication or human-machine communication systems concerns speech enhancement in noise. In this domain, a certain number of techniques exist, all of them based on an acoustic-only approach--that is, the processing of the audio corrupted signal using audio information (from the corrupted signal only or additive audio information). In this paper, an audio-visual approach ...

In current scenario of modern technology, we are facing a necessity of noise removal in signal processing. Various approaches are used for the same. This paper describes an intelligent adaptive filtering for noise cancellation. Here ANFIS method is being used for removal of noise from audio speech signals. An audio signal contaminated with noise is taken and inspected with eight types of member...

Film restoration using image processing, has been an active research field during the last years. However, the restoration of the soundtrack has been mainly performed in the sound domain, using signal processing methods, despite the fact that it is recorded as a continuous image between the images of the film and the perforations. While the very few published approaches focus on removing dust p...

This paper describes our work on the development of a low latency stream-based audio pre-processing system for broadcast news using model-based techniques. It performs speech/nonspeech classification, speaker segmentation, speaker clustering, gender and background conditions classification. As a way to increase the modelling accuracy our algorithms make extensive use of Artificial Neural Networ...

We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation process similar to image super-resolution. Our method is simple and does not i...

In a variety of applications of audio signal processing, for example blind source separation (BSS), harmonic signals need to be treated differently from percussive signals. Thus, recognizing if a signal is harmonic or percussive is a very helpful and frequently used preprocessing step. Different measures have been proposed to capture either the harmonicity or the percussivity of a signal, using...

Abstract The research of differential equation models in music creation and audio is a current hotspot. This paper uses homogeneous first-order linear partial equations to analyze the relevant signals creation. focuses on analyzing expression based time series. discusses its characteristic function conducts modeling signals. method successfully established string vibration model time-series aut...