Experimental evaluation of the joint position-pitch estimation (POPI) algorithm in noisy environments

Habib, Tufail; Képesi, Marián; Ottowitz, L.

doi:10.1109/sam.2008.4606892

Cited by 3 publications

(9 citation statements)

References 7 publications

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“…For a single speaker scenario and clean speech recordings, the basic DOA and pitch estimation algorithm in [23] performs quite well. However, its performance decreases in noisy and reverberant conditions as well as in multi-speaker scenarios.…”

Section: Methods To Increase the Robustnessmentioning

confidence: 99%

“…A cepstral weighting (cf. Figure 1) of the 2D pattern ρ i (ϕ, f 0 ), based on the cepstrum of the cross-correlation, was proposed in [23] to further increase the pitch estimation for disturbed input signals. The cepstrum is computed on the inverse STFT of the logarithm of the amplitude of the spectrum y i [n].…”

Section: Joint Doa and Pitch Estimationmentioning

confidence: 99%

“…In [22], a joint position and pitch (PoPi) estimation method has been proposed which is based on either cross-correlations or crosspower spectral densities (CPSDs). Several extensions have been proposed using cepstral weighting [23], gammatonelike weighting [24], time-domain GCC-PHAT replacement [25], particle filtering [26], and speaker-dependent subgrouping [27]. In [28], a different method based on a recurrent timing neural network is used for joint DOA and pitch estimation.…”

Section: Introductionmentioning

confidence: 99%

“…We have taken the CPSD-based method proposed in [22] combined with cepstral weighting [23], gammatone-like weighting [24], and a subsequent particle filtering [26] as the core algorithm, and we propose several extensions to improve both accuracy and robustness in this paper. As a first extension, a frequency-domain comb filter is introduced to improve the performance for simultaneously active speakers.…”

Section: Introductionmentioning

confidence: 99%

“…Since we are interested in joint DOA and pitch estimation, the main feature of the algorithm is the computation of a two-dimensional (2D) pattern for DOA and pitch. As core algorithm, the CPSD-based method proposed in [22] combined with cepstral weighting [23] and gammatone-like weighting [24] is used. To enable speaker tracking, a subsequent particle filter [26] is also part of the core algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Joint estimation of pitch and direction of arrival: improving robustness and accuracy for multi-speaker scenarios

Gerlach

Bitzer

Goetze

et al. 2014

J AUDIO SPEECH MUSIC PROC.

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In many speech communication applications, robust localization and tracking of multiple speakers in noisy and reverberant environments are of major importance. Several algorithms to tackle this problem have been proposed in the last decades. In this paper, we propose several extensions to a recently presented joint direction of arrival (DOA) and pitch estimation method, increasing its robustness in multi-speaker scenarios, noise, and reverberation. First, a spectral comb filter is added to the original algorithm to better cope with concurrent speakers. Second, the well-known generalized cross-correlation with phase transform (GCC-PHAT) is used as an additional weighting function to improve the DOA estimation accuracy in terms of correct hits. Third, using multiple microphone pairs, the multi-channel cross-correlation approach is incorporated to improve the robustness against noise and reverberation. In order to improve tracking for moving and even intersecting speakers, a particle filter is used. Experiments with real-world recordings in realistic acoustic conditions show that the proposed extensions increase the DOA hit rate by about 33% compared to the original algorithm for two step-wise moving sources at a signal-to-noise ratio (SNR) of 15 dB and a reverberation time RT 60 of 560 ms.

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Section: Methods To Increase the Robustnessmentioning

confidence: 99%

Section: Joint Doa and Pitch Estimationmentioning

confidence: 99%