Joint Position-Pitch Tracking for 2-Channel Audio

Képesi, Marián; Pernkopf, Franz; Wohlmayr, Michael

doi:10.1109/cbmi.2007.385426

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…This method shows degradation in performance when the room reverberation is high. As the room reverberation increases, this method shows degradations in performance (Kepesi et al 2007). Since the spectral characteristics of the received signal are affected by the multipath propagation or reverberation in a room, the GCC function is made more robust by deemphasizing the frequency-dependent weighting (Raykar et al 2005).…”

Section: Gcc-maximum Likelihoodmentioning

confidence: 97%

Performance of speaker localization using microphone array

Visalakshi

Dhanalakshmi

Palanivel

2016

Int J Speech Technol

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Speaker localization is a technique to locate and track an active speaker from multiple acoustic sources using microphone array. Microphone array is used to improve the speech quality of recorded speech signal in meeting room and other places. In this work, the time delay estimation between source and each microphone is calculated using a localization method called time differences of arrival (TDOA). TDOA localization consists of two steps namely (a) a time delay estimator and (b) a localization estimator. For time delay estimation, the generalized crosscorrelation using phase transform, the generalized cross correlation using maximum likelihood, linear prediction (LP) residual and the Hilbert envelope of the LP residual are chosen for estimating the location of a person. A new speaker localization algorithm known as group search optimization (GSO) algorithm is proposed. The performance of this algorithm is analyzed and compared with Gauss-Newton nonlinear least square method and genetic algorithm. Experimental results show that the proposed GSO method outperforms the other methods in terms of mean square error, root mean square error, mean absolute error, mean absolute percentage error, euclidean distance and mean absolute relative error.Keywords Speaker localization Á Microphone array Á Time difference of arrival Á Gauss-Newton nonlinear least square method Á Genetic algorithm and group search optimization algorithm

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Section: Gcc-maximum Likelihoodmentioning

confidence: 97%

Performance of speaker localization using microphone array

Visalakshi

Dhanalakshmi

Palanivel

2016

Int J Speech Technol

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“…Despite the host of methods devoted to pitch estimation, it appears that very few methods have been devised for estimating the pitch when multiple channels are available, as would be the case in microphone array processing or in studio recordings of music. Some methods do exist, however, including those of [8,9] and the joint pitch and localization methods of [10,11]. There are several reasons why multi-channel pitch estimation should be pursued when multiple channels are available.…”

Section: Introductionmentioning

confidence: 98%

Multi-channel maximum likelihood pitch estimation

Christensen

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, a method for multi-channel pitch estimation is proposed. The method is a maximum likelihood estimator and is based on a parametric model where the signals in the various channels share the same fundamental frequency but can have different amplitudes, phases, and noise characteristics. This essentially means that the model allows for different conditions in the various channels, like different signalto-noise ratios, microphone characteristics and reverberation. Moreover, the method does not assume that a certain array structure is used but rather relies on a more general model and is hence suited for a large class of problems. Simulations with real signals shows that the method outperforms a stateof-the-art multi-channel method in terms of gross error rate.

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“…For a speech signal embedded in realistic acoustic environments, harmonicity and binaural cues of the talker co-vary with its spatial position and with those of interfering signals. The computational ASA models of Kepesi et al (2007) and Ma et al (2007) therefore explicitly assume combined processing of the two cues. The mechanisms of combining harmonic and binaural cues employed in these model studies have been shown to be effective in performing ASA and have been optimized using signal-processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Combination of binaural and harmonic masking release effects in the detection of a single component in complex tones

2018

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Both harmonic and binaural signal properties are relevant for auditory processing. To investigate how these cues combine in the auditory system, detection thresholds for an 800-Hz tone masked by a diotic (i.e., identical between the ears) harmonic complex tone were measured in six normal-hearing subjects. The target tone was presented either diotically or with an interaural phase difference (IPD) of 180° and in either harmonic or "mistuned" relationship to the diotic masker. Three different maskers were used, a resolved and an unresolved complex tone (fundamental frequency: 160 and 40 Hz) with four components below and above the target frequency and a broadband unresolved complex tone with 12 additional components. The target IPD provided release from masking in most masker conditions, whereas mistuning led to a significant release from masking only in the diotic conditions with the resolved and the narrowband unresolved maskers. A significant effect of mistuning was neither found in the diotic condition with the wideband unresolved masker nor in any of the dichotic conditions. An auditory model with a single analysis frequency band and different binaural processing schemes was employed to predict the data of the unresolved masker conditions. Sensitivity to modulation cues was achieved by including an auditorymotivated modulation filter in the processing pathway. The predictions of the diotic data were in line with the experimental results and literature data in the narrowband condition, but not in the broadband condition, suggesting that across-frequency processing is involved in processing modulation information. The experimental and model results in the dichotic conditions show that the binaural processor cannot exploit modulation information in binaurally unmasked conditions. PACS numbers: 43.66.Pn (binaural hearing), 43.66.Ba (models and theories of auditory processes), 43.66.Hg (pitch), 43.66.Dc (masking)

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Joint Position-Pitch Tracking for 2-Channel Audio

Cited by 15 publications

References 6 publications

Performance of speaker localization using microphone array

Performance of speaker localization using microphone array

Multi-channel maximum likelihood pitch estimation

Combination of binaural and harmonic masking release effects in the detection of a single component in complex tones

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