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.

Multi-speed DSP kernel and clock mechanism

Abstract: The processing speed of a digital signal processor or system processor is controlled in accordance with the functions required in a task to be performed by the device, with these functions being compared to a table of maximum processing speeds at which various functions can be performed reliably by the device. This method is applied to a number of digital signal processors on a communications adapter, with a core kernel of each of these digital signal processors being driven at a processing speed controlled in this way, while peripheral functions of all these digital signal processors are performed according to a clock signal synchronized with data being received from a network transmission line.

Emergency radio frequency warning device

Abstract: An electronic whistle in the form of broad band radio frequency interference generator. A broad band generator generates a coherent or random noise signal which is modulated by an audio signal which corresponds to an audible warning signal such as a whistle or a siren. The modulated signal is then utilized to modulate a carrier signal and amplified and transmitted so that the radios in vehicles in the immediate area will receive an audible interference signal regardless of the particular channel to which the radio is tuned. The audible interference signal may be an intelligible reproduction of the input audio signal so that the driver of the vehicle can determine whether the warning signal is generated by a train whistle, a siren or a human voice.

Speakers Role Recognition in Multiparty Audio Recordings Using Social Network Analysis and Duration Distribution Modeling

Abstract: This paper presents two approaches for speaker role recognition in multiparty audio recordings. The experiments are performed over a corpus of 96 radio bulletins corresponding to roughly 19 h of material. Each recording involves, on average, 11 speakers playing one among six roles belonging to a predefined set. Both proposed approaches start by segmenting automatically the recordings into single speaker segments, but perform role recognition using different techniques. The first approach is based on Social Network Analysis, the second relies on the intervention duration distribution across different speakers. The two approaches are used separately and combined and the results show that around 85% of the recording time can be labeled correctly in terms of role.

LyricSynchronizer: Automatic Synchronization System Between Musical Audio Signals and Lyrics

Abstract: This paper describes a system that can automatically synchronize polyphonic musical audio signals with their corresponding lyrics. Although methods for synchronizing monophonic speech signals and corresponding text transcriptions by using Viterbi alignment techniques have been proposed, these methods cannot be applied to vocals in CD recordings because vocals are often overlapped by accompaniment sounds. In addition to a conventional method for reducing the influence of the accompaniment sounds, we therefore developed four methods to overcome this problem: a method for detecting vocal sections, a method for constructing robust phoneme networks, a method for detecting fricative sounds, and a method for adapting a speech-recognizer phone model to segregated vocal signals. We then report experimental results for each of these methods and also describe our music playback interface that utilizes our system for synchronizing music and lyrics.

Automatic transcription of drum loops

Abstract: Recent efforts in audio indexing and retrieval in music databases mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembe. Most studies of drum signal transcription focus on sounds taken in isolation. In this paper, we propose several methods for drum loop transcription where the drums signals dataset reflects the variability encountered in modern audio recordings (real and natural drum kits, audio effects, simultaneous instruments, etc.). The approaches described are based on hidden Markov models (HMM) and support vector machines (SVM). Promising results are obtained with a 83.9% correct recognition rate for a simplified taxonomy.