Audio signal processing

About: Audio signal processing is a research topic. Over the lifetime, 21463 publications have been published within this topic receiving 319597 citations. The topic is also known as: audio processing & Acoustic signal processing.

Method for equalizing audio, and video apparatus using the same

Abstract: A method for equalizing audio and a video apparatus using the audio equalizing method are provided. The method for equalizing audio includes detecting the distance between a speaker mounted in a video apparatus and a reflective surface, and equalizing an audio signal to be output from the speaker based on the detected distance. Accordingly, attenuation of audio output is reduced, so audio output is optimized.

Multi-resolution linear prediction based features for audio onset detection with bidirectional LSTM neural networks

Abstract: A plethora of different onset detection methods have been proposed in the recent years. However, few attempts have been made with respect to widely-applicable approaches in order to achieve superior performances over different types of music and with considerable temporal precision. In this paper, we present a multi-resolution approach based on discrete wavelet transform and linear prediction filtering that improves time resolution and performance of onset detection in different musical scenarios. In our approach, wavelet coefficients and forward prediction errors are combined with auditory spectral features and then processed by a bidirectional Long Short-Term Memory recurrent neural network, which acts as reduction function. The network is trained with a large database of onset data covering various genres and onset types. We compare results with state-of-the-art methods on a dataset that includes Bello, Glover and ISMIR 2004 Ballroom sets, and we conclude that our approach significantly outperforms existing methods in terms of F-Measure. For pitched non percussive music an absolute improvement of 7.5% is reported.

A simple but efficient real-time Voice Activity Detection algorithm

Abstract: Voice Activity Detection (VAD) is a very important front end processing in all Speech and Audio processing applications. The performance of most if not all speech/audio processing methods is crucially dependent on the performance of Voice Activity Detection. An ideal voice activity detector needs to be independent from application area and noise condition and have the least parameter tuning in real applications. In this paper a nearly ideal VAD algorithm is proposed which is both easy-to-implement and noise robust, comparing to some previous methods. The proposed method uses short-term features such as Spectral Flatness (SF) and Short-term Energy. This helps the method to be appropriate for online processing tasks. The proposed method was evaluated on several speech corpora with additive noise and is compared with some of the most recent proposed algorithms. The experiments show satisfactory performance in various noise conditions.

Radio transmitter/receiver for digital and analog communications system

Abstract: According to the present invention, a radio apparatus has a section for receiving an analog signal and a digital angle-modulated carrier wave signal. The analog signal and digital angle-modulated carrier wave output of the receiver section are demodulated to provide first and second demodulated signals. A clock signal is regenerated from the output of either the demodulating means or receiver section. A control signal selectively operates a switch for passing either the first or the second demodulated signals. The regenerated clock signal controls the switch.

A Framework for Invertible, Real-Time Constant-Q Transforms

Abstract: Audio signal processing frequently requires time-frequency representations and in many applications, a non-linear spacing of frequency bands is preferable. This paper introduces a framework for efficient implementation of invertible signal transforms allowing for non-uniform frequency resolution. Non-uniformity in frequency is realized by applying nonstationary Gabor frames with adaptivity in the frequency domain. The realization of a perfectly invertible constant-Q transform is described in detail. To achieve real-time processing, independent of signal length, slice-wise processing of the full input signal is proposed and referred to as sliCQ transform. By applying frame theory and FFT-based processing, the presented approach overcomes computational inefficiency and lack of invertibility of classical constant-Q transform implementations. Numerical simulations evaluate the efficiency of the proposed algorithm and the method's applicability is illustrated by experiments on real-life audio signals .