{"title":"Cross Pattern Coherence Algorithm for Spatial Filtering Applications Utilizing Microphone Arrays","authors":"Symeon Delikaris-Manias, V. Pulkki","doi":"10.1109/TASL.2013.2277928","DOIUrl":null,"url":null,"abstract":"A parametric spatial filtering algorithm with a fixed beam direction is proposed in this paper. The algorithm utilizes the normalized cross-spectral density between signals from microphones of different orders as a criterion for focusing in specific directions. The correlation between microphone signals is estimated in the time-frequency domain. A post-filter is calculated from a multichannel input and is used to assign attenuation values to a coincidentally captured audio signal. The proposed algorithm is simple to implement and offers the capability of coping with interfering sources at different azimuthal locations with or without the presence of diffuse sound. It is implemented by using directional microphones placed in the same look direction and have the same magnitude and phase response. Experiments are conducted with simulated and real microphone arrays employing the proposed post-filter and compared to previous coherence-based approaches, such as the McCowan post-filter. A significant improvement is demonstrated in terms of objective quality measures. Formal listening tests conducted to assess the audibility of artifacts of the proposed algorithm in real acoustical scenarios show that no annoying artifacts existed with certain spectral floor values. Examples of the proposed algorithm can be found online at http://www.acoustics.hut.fi/projects/cropac/soundExamples.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2277928","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2013.2277928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
A parametric spatial filtering algorithm with a fixed beam direction is proposed in this paper. The algorithm utilizes the normalized cross-spectral density between signals from microphones of different orders as a criterion for focusing in specific directions. The correlation between microphone signals is estimated in the time-frequency domain. A post-filter is calculated from a multichannel input and is used to assign attenuation values to a coincidentally captured audio signal. The proposed algorithm is simple to implement and offers the capability of coping with interfering sources at different azimuthal locations with or without the presence of diffuse sound. It is implemented by using directional microphones placed in the same look direction and have the same magnitude and phase response. Experiments are conducted with simulated and real microphone arrays employing the proposed post-filter and compared to previous coherence-based approaches, such as the McCowan post-filter. A significant improvement is demonstrated in terms of objective quality measures. Formal listening tests conducted to assess the audibility of artifacts of the proposed algorithm in real acoustical scenarios show that no annoying artifacts existed with certain spectral floor values. Examples of the proposed algorithm can be found online at http://www.acoustics.hut.fi/projects/cropac/soundExamples.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.