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.

Audio and video digital recording and playback system

Abstract: A microcomputer system for converting an analog signal, such as an audio or video signal representative of sound or video into a digital form for storing in digital form in a highly condensed code and for reconstructing the analog signal from the coded digital form. The system includes reductive analytic means where the original digital data stream is converted to a sequential series of spectrograms, signal amplitude histrograms and waveform code tables. Approximately 100 times less storage space than previously required for the storage of digitized signals is thereby obtained. Additive synthesis logic interprets the stored codes and recreates an output digital data stream for digital to analog conversion that is nearly identical to the original analog signal.

Apparatus and method for robust classification of audio signals, and method for establishing and operating an audio-signal database, as well as computer program

Abstract: An apparatus for producing a fingerprint signal from an audio signal includes a means for calculating energy values for frequency bands of segments of the audio signal which are successive in time, so as to obtain, from the audio signal, a sequence of vectors of energy values, a means for scaling the energy values to obtain a sequence of scaled vectors, and a means for temporal filtering of the sequence of scaled vectors to obtain a filtered sequence which represents the fingerprint, or from which the fingerprint may be derived. Thus, a fingerprint is produced which is robust against disturbances due to problems associated with coding or with transmission channels, and which is especially suited for mobile radio applications.

Energy-efficient unobtrusive identification of a speaker

Abstract: Functionality is described herein for recognizing speakers in an energy-efficient manner The functionality employs a heterogeneous architecture that comprises at least a first processing unit and a second processing unit The first processing unit handles a first set of audio processing tasks (associated with the detection of speech) while the second processing unit handles a second set of audio processing tasks (associated with the identification of speakers), where the first set of tasks consumes less power than the second set of tasks The functionality also provides unobtrusive techniques for collecting audio segments for training purposes The functionality also encompasses new applications which may be invoked in response to the recognition of speakers

Sound Coding in Cochlear Implants: From electric pulses to hearing

Abstract: Cochlear implantation is a life-changing intervention for people with a severe hearing impairment [1]. For most cochlear implant (CI) users, speech intelligibility is satisfactory in quiet environments. Although modern CIs provide up to 22 stimulation channels, information transfer is still limited for the perception of fine spectrotemporal details in many types of sound. These details contribute to the perception of music and speech in common listening situations, such as where background noise is present. Over the past several decades, many different sound processing strategies have been developed to provide more details about acoustic signals to CI users. In this article, progress in sound coding for CIs is reviewed. Starting from a basic strategy, the current commercially most-used signal processing schemes are discussed, as well as recent developments in coding strategies that aim to improve auditory perception. This article focuses particularly on the stimulation strategies, which convert sound signals into patterns of nerve stimulation. The neurophysiological rationale behind some of these strategies is discussed and aspects of CI performance that require further improvement are identified.

Context-based speech recognition

Abstract: A processing system receives an audio signal encoding a portion of an utterance. The processing system receives context information associated with the utterance, wherein the context information is not derived from the audio signal or any other audio signal. The processing system provides, as input to a neural network, data corresponding to the audio signal and the context information, and generates a transcription for the utterance based on at least an output of the neural network.