@InProceedings{8081262,
  author = {R. G. Borin and M. T. M. Silva},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Voice activity detection using discriminative restricted Boltzmann machines},
  year = {2017},
  pages = {523-527},
  abstract = {Voice Activity Detection (VAD) plays an important role in current technological applications, such as wireless communications and speech recognition. In this paper, we address the VAD task through machine learning by using a discriminative restricted Boltzmann machine (DRBM). We extend the conventional DRBM to deal with continuous-valued data and employ feature vectors based either on mel-frequency cepstral coefficients or on filter-bank energies. The resulting detector slightly outperforms the VAD often used as benchmark for detector comparison. Results also indicate that DRBM is able to deal with strongly correlated feature vectors.},
  keywords = {Boltzmann machines;feature extraction;learning (artificial intelligence);speech recognition;voice activity detection;discriminative restricted Boltzmann machine;current technological applications;wireless communications;speech recognition;VAD task;machine learning;conventional DRBM;DRBM;Detectors;Training;Speech;Signal to noise ratio;Cepstral analysis},
  doi = {10.23919/EUSIPCO.2017.8081262},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347672.pdf},
}

{"_id":"ARYfGo4rPdXaZ7LL8","bibbaseid":"borin-silva-voiceactivitydetectionusingdiscriminativerestrictedboltzmannmachines-2017","authorIDs":[],"author_short":["Borin, R. G.","Silva, M. T. M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["R.","G."],"propositions":[],"lastnames":["Borin"],"suffixes":[]},{"firstnames":["M.","T.","M."],"propositions":[],"lastnames":["Silva"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Voice activity detection using discriminative restricted Boltzmann machines","year":"2017","pages":"523-527","abstract":"Voice Activity Detection (VAD) plays an important role in current technological applications, such as wireless communications and speech recognition. In this paper, we address the VAD task through machine learning by using a discriminative restricted Boltzmann machine (DRBM). We extend the conventional DRBM to deal with continuous-valued data and employ feature vectors based either on mel-frequency cepstral coefficients or on filter-bank energies. The resulting detector slightly outperforms the VAD often used as benchmark for detector comparison. Results also indicate that DRBM is able to deal with strongly correlated feature vectors.","keywords":"Boltzmann machines;feature extraction;learning (artificial intelligence);speech recognition;voice activity detection;discriminative restricted Boltzmann machine;current technological applications;wireless communications;speech recognition;VAD task;machine learning;conventional DRBM;DRBM;Detectors;Training;Speech;Signal to noise ratio;Cepstral analysis","doi":"10.23919/EUSIPCO.2017.8081262","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347672.pdf","bibtex":"@InProceedings{8081262,\n author = {R. G. Borin and M. T. M. Silva},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Voice activity detection using discriminative restricted Boltzmann machines},\n year = {2017},\n pages = {523-527},\n abstract = {Voice Activity Detection (VAD) plays an important role in current technological applications, such as wireless communications and speech recognition. In this paper, we address the VAD task through machine learning by using a discriminative restricted Boltzmann machine (DRBM). We extend the conventional DRBM to deal with continuous-valued data and employ feature vectors based either on mel-frequency cepstral coefficients or on filter-bank energies. The resulting detector slightly outperforms the VAD often used as benchmark for detector comparison. Results also indicate that DRBM is able to deal with strongly correlated feature vectors.},\n keywords = {Boltzmann machines;feature extraction;learning (artificial intelligence);speech recognition;voice activity detection;discriminative restricted Boltzmann machine;current technological applications;wireless communications;speech recognition;VAD task;machine learning;conventional DRBM;DRBM;Detectors;Training;Speech;Signal to noise ratio;Cepstral analysis},\n doi = {10.23919/EUSIPCO.2017.8081262},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347672.pdf},\n}\n\n","author_short":["Borin, R. G.","Silva, M. T. M."],"key":"8081262","id":"8081262","bibbaseid":"borin-silva-voiceactivitydetectionusingdiscriminativerestrictedboltzmannmachines-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347672.pdf"},"keyword":["Boltzmann machines;feature extraction;learning (artificial intelligence);speech recognition;voice activity detection;discriminative restricted Boltzmann machine;current technological applications;wireless communications;speech recognition;VAD task;machine learning;conventional DRBM;DRBM;Detectors;Training;Speech;Signal to noise ratio;Cepstral analysis"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.548Z","downloads":0,"keywords":["boltzmann machines;feature extraction;learning (artificial intelligence);speech recognition;voice activity detection;discriminative restricted boltzmann machine;current technological applications;wireless communications;speech recognition;vad task;machine learning;conventional drbm;drbm;detectors;training;speech;signal to noise ratio;cepstral analysis"],"search_terms":["voice","activity","detection","using","discriminative","restricted","boltzmann","machines","borin","silva"],"title":"Voice activity detection using discriminative restricted Boltzmann machines","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}