Identifying acquisition devices from recorded speech signals using   wavelet-based features

ÖMER ESKİDERE

Makale detayları

Identifying acquisition devices from recorded speech signals using wavelet-based features

Yazarlar:

ÖMER ESKİDERE

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 3, pp.1942-1954

Belge Tipi:

Makale

Yayın Tarihi:

5/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000742195

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Speech characteristics have played a critical role in   media forensics, particularly in the investigation of evidence. This study   proposes two wavelet-based feature extraction methods for the identification   of acquisition devices from recorded speech. These methods are discrete   wavelet-based coefficients (DWBCs) and wavelet packet-based coefficients,   which are mainly based on a multiresolution analysis. These features'   ability to capture characteristics of acquisition devices is compared to   conventional mel frequency cepstral coefficients and subband-based   coefficients. In the experiments, 14 different audio acquisition devices   were trained and tested using support vector machines. Experimental results   showed that DWBCs can effectively be used in source audio acquisition device   identification problems.

Makale detayları

Identifying acquisition devices from recorded speech signals using wavelet-based features

Yazarlar: ÖMER ESKİDERE

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 3, pp.1942-1954

Belge Tipi: Makale

Yayın Tarihi: 5/2016

Belge Dili: Türkçe

Document ID (Org No.): 2000742195

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

ÖMER ESKİDERE

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 3, pp.1942-1954

Belge Tipi:

Makale

Yayın Tarihi:

5/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000742195

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632