HFCC based recognition of bird species

Wielgat, Robert; Zieliński, Tomasz P.; Potempa, Tomasz; Lisowska-Lis, Agnieszka; Król, Daniel

doi:10.1109/spa.2007.5903313

Cited by 12 publications

(11 citation statements)

References 10 publications

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“…This finding is consistent with our former results [11,12] , and with research obtained for English speech recognition [2] as well as with the results we obtained for the recognition of bird voices [13] . In the case of substitution detection, recognition accuracy can be additionally enhanced by choosing optimal ERB scale factors for different phoneme pairs.…”

Section: Discussionsupporting

confidence: 94%

Automatic Recognition of Pathological Phoneme Production

Wielgat

Zieliński

Woźniak

et al. 2008

Folia Phoniatr Logop

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The superiority of HFCC features over those of MFCC was demonstrated. Results obtained by DTW methods, mainly by modified phoneme-based DTW classifier, were slightly better in comparison with the HMM classifier. Results obtained for the detection of substitution in pairs (for the correct phonetic charactors please see online article) are very promising. The methods developed for these cases can be integrated into computer systems for speech therapy. For substitutions in pairs (for the correct phonetic charactors please see online article) further research is necessary.

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Section: Discussionsupporting

confidence: 94%

Automatic Recognition of Pathological Phoneme Production

Wielgat

Zieliński

Woźniak

et al. 2008

Folia Phoniatr Logop

Self Cite

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“…The cited papers as well as the authors' previous experience [6] confirm that statistical framework based on Gaussian mixture models is a suitable tool for building acoustic models representing bird sounds, for which reason [3][4][5]8] parameters are commonly used. Among other feature extractors, cepstral coefficients (CC) [7] and human-factor cepstral coefficients (HFCC) [8] are also encountered.…”

Section: Introductionmentioning

confidence: 66%

“…Among other feature extractors, cepstral coefficients (CC) [7] and human-factor cepstral coefficients (HFCC) [8] are also encountered. It should be noted however that their optimality in representation of bird sounds is still an open research question.…”

Section: Introductionmentioning

confidence: 99%

On using prefiltration in HMM-based bird species recognition

Wielgat

Swietojanski

Potempa

et al. 2012

2012 International Conference on Signals and Electronic Systems (ICSES)

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Automatic bird species recognition method using their voices is presented in this paper. The selected bird species have been detected by hidden Markov models (HMM) classifier using Mel-frequency cepstral coefficients (MFCC). In order to support recognition process, analysed signals have been appropriately filtered before classification in the so called prefiltration process. The prefiltration strategy assumed using n-th order IIR Butterworth filter bank. Each filter from the filter bank was applied for band pass filtration in the bird species specific and signal type band. Increase of recognition accuracy has been observed in case of prefiltration with properly chosen filter order. Experiments have been carried out on the set of bird voices containing 30 bird species, one of which is endangered with extinction.

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“…As a result of using the approach, HFCCs are perceived as a better approximation of human auditory model [1], [10] than MFCCs and by incorporating appropriate scaling to ERB, by some factor, HFCC-based speech recognition can be under some circumstances treated as robust speech features to chosen applications [12].…”

Section: A Scenario and Speech Featuresmentioning

confidence: 99%

“…On the basis of literature search, especially [1], [2], [3], [11], [12] and performed experiments, the following algorithm has been proposed to unsupervised key detection. Description of respective processing blocks, presented in Fig.…”

Section: A Algorithmmentioning

confidence: 99%

Word detection in recorded speech using textual queries

Laszko

2015

Annals of Computer Science and Information Systems

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Abstract-The paper presents unsupervised method for word detection in recorded spoken language signal. The method is based on examining signal similarity of two analyzed media description: registered voice and a word (textual query) synthesized by using Text-to-Speech tools. The descriptions of media were given by a sequence of Mel-Frequency Cepstral Coefficients or Human-Factor Cepstral Coefficients. Dynamic Time Warping algorithm has been applied to provide time alignment of the given media description. The detection involved classification method based on cost function, calculated upon signal similarity and alignment path. Potential false matches were eliminated in the algorithm by comparing costs of the path subsequences to a threshold value. The results of the work could provide incentives to build affordable commercial or non-commercial solutions for specific and multilingual applications.Index Terms-Speech processing, speech analysis, pattern matching, keyword search, audio information retrieval

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HFCC based recognition of bird species

Cited by 12 publications

References 10 publications

Automatic Recognition of Pathological Phoneme Production

Automatic Recognition of Pathological Phoneme Production

On using prefiltration in HMM-based bird species recognition

Word detection in recorded speech using textual queries

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