@InProceedings{6952299,
  author = {J. R. Jensen and M. G. Christensen},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {DOA and pitch estimation of audio sources using IAA-based filtering},
  year = {2014},
  pages = {900-904},
  abstract = {For decades, it has been investigated how to separately solve the problems of both direction-of-arrival (DOA) and pitch estimation. Recently, it was found that estimating these parameters jointly from multichannel recordings of audio can be extremely beneficial. Many joint estimators are based on knowledge of the inverse sample covariance matrix. Typically, this covariance is estimated using the sample covariance matrix, but for this estimate to be full rank, many temporal samples are needed. In cases with non-stationary signals, this is a serious limitation. We therefore investigate how a recent joint DOA and pitch filtering-based estimator can be combined with the iterative adaptive approach to circumvent this limitation in joint DOA and pitch estimation of audio sources. Simulations show a clear improvement compared to when using the sample covariance matrix and the considered approach also outperforms other state-of-the-art methods. Finally, the applicability of the considered approach is verified on real speech.},
  keywords = {audio signal processing;covariance matrices;direction-of-arrival estimation;filtering theory;iterative methods;microphone arrays;DOA;pitch estimation;IAA-based filtering;direction-of-arrival estimation;multichannel recordings;inverse sample covariance matrix;filtering-based estimator;iterative adaptive approach;audio sources;covariance matrix;iterative adaptive approach;Direction-of-arrival estimation;Estimation;Joints;Covariance matrices;Microphones;Speech;Harmonic analysis;Direction-of-arrival;fundamental frequency;linearly constrained minimum variance;iterative adaptive approach;high resolution},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569921261.pdf},
}

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Typically, this covariance is estimated using the sample covariance matrix, but for this estimate to be full rank, many temporal samples are needed. In cases with non-stationary signals, this is a serious limitation. We therefore investigate how a recent joint DOA and pitch filtering-based estimator can be combined with the iterative adaptive approach to circumvent this limitation in joint DOA and pitch estimation of audio sources. Simulations show a clear improvement compared to when using the sample covariance matrix and the considered approach also outperforms other state-of-the-art methods. Finally, the applicability of the considered approach is verified on real speech.","keywords":"audio signal processing;covariance matrices;direction-of-arrival estimation;filtering theory;iterative methods;microphone arrays;DOA;pitch estimation;IAA-based filtering;direction-of-arrival estimation;multichannel recordings;inverse sample covariance matrix;filtering-based estimator;iterative adaptive approach;audio sources;covariance matrix;iterative adaptive approach;Direction-of-arrival estimation;Estimation;Joints;Covariance matrices;Microphones;Speech;Harmonic analysis;Direction-of-arrival;fundamental frequency;linearly constrained minimum variance;iterative adaptive approach;high resolution","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569921261.pdf","bibtex":"@InProceedings{6952299,\n author = {J. R. Jensen and M. G. Christensen},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {DOA and pitch estimation of audio sources using IAA-based filtering},\n year = {2014},\n pages = {900-904},\n abstract = {For decades, it has been investigated how to separately solve the problems of both direction-of-arrival (DOA) and pitch estimation. Recently, it was found that estimating these parameters jointly from multichannel recordings of audio can be extremely beneficial. Many joint estimators are based on knowledge of the inverse sample covariance matrix. Typically, this covariance is estimated using the sample covariance matrix, but for this estimate to be full rank, many temporal samples are needed. In cases with non-stationary signals, this is a serious limitation. We therefore investigate how a recent joint DOA and pitch filtering-based estimator can be combined with the iterative adaptive approach to circumvent this limitation in joint DOA and pitch estimation of audio sources. Simulations show a clear improvement compared to when using the sample covariance matrix and the considered approach also outperforms other state-of-the-art methods. 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