{"_id":"cAa4Ruw5v97FdAtc9","bibbaseid":"aylln-gilpita-utrillamanso-rosazurera-acomputationallyefficientsinglechannelspeechenhancementalgorithmformonauralhearingaids-2014","authorIDs":[],"author_short":["Ayllón, D.","Gil-Pita, R.","Utrilla-Manso, M.","Rosa-Zurera, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["D."],"propositions":[],"lastnames":["Ayllón"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Gil-Pita"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Utrilla-Manso"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Rosa-Zurera"],"suffixes":[]}],"booktitle":"2014 22nd European Signal Processing Conference (EUSIPCO)","title":"A computationally-efficient single-channel speech enhancement algorithm for monaural hearing aids","year":"2014","pages":"2050-2054","abstract":"A computationally-efficient single-channel speech enhancement algorithm to improve intelligibility in monaural hearing aids is presented in this paper. The algorithm combines a novel set of features with a simple supervised machine learning technique to estimate the frequency-domain Wiener filter for noise reduction, using extremely low computational resources. Results show a noticeable intelligibility improvement in terms of PESQ score and SNRESI, even for low input SNR, using only a 7% of the computational resources available in a state-of-the-art commercial hearing aid. The performance of the algorithm is comparable to the performance of current algorithms that use more computationally complex features and learning schemas.","keywords":"computational complexity;hearing aids;learning (artificial intelligence);speech enhancement;speech intelligibility;Wiener filters;computationally efficient single channel speech enhancement algorithm;monaural hearing aids;supervised machine learning technique;frequency domain Wiener filter;noise reduction;intelligibility improvement;Speech;Speech enhancement;Noise measurement;Signal processing algorithms;Signal to noise ratio;Training;Speech enhancement;Noise reduction;Time-frequency masking;Supervised learning","issn":"2076-1465","month":"Sep.","bibtex":"@InProceedings{6952750,\n author = {D. Ayllón and R. Gil-Pita and M. Utrilla-Manso and M. Rosa-Zurera},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {A computationally-efficient single-channel speech enhancement algorithm for monaural hearing aids},\n year = {2014},\n pages = {2050-2054},\n abstract = {A computationally-efficient single-channel speech enhancement algorithm to improve intelligibility in monaural hearing aids is presented in this paper. The algorithm combines a novel set of features with a simple supervised machine learning technique to estimate the frequency-domain Wiener filter for noise reduction, using extremely low computational resources. Results show a noticeable intelligibility improvement in terms of PESQ score and SNRESI, even for low input SNR, using only a 7% of the computational resources available in a state-of-the-art commercial hearing aid. The performance of the algorithm is comparable to the performance of current algorithms that use more computationally complex features and learning schemas.},\n keywords = {computational complexity;hearing aids;learning (artificial intelligence);speech enhancement;speech intelligibility;Wiener filters;computationally efficient single channel speech enhancement algorithm;monaural hearing aids;supervised machine learning technique;frequency domain Wiener filter;noise reduction;intelligibility improvement;Speech;Speech enhancement;Noise measurement;Signal processing algorithms;Signal to noise ratio;Training;Speech enhancement;Noise reduction;Time-frequency masking;Supervised learning},\n issn = {2076-1465},\n month = {Sep.},\n}\n\n","author_short":["Ayllón, D.","Gil-Pita, R.","Utrilla-Manso, M.","Rosa-Zurera, M."],"key":"6952750","id":"6952750","bibbaseid":"aylln-gilpita-utrillamanso-rosazurera-acomputationallyefficientsinglechannelspeechenhancementalgorithmformonauralhearingaids-2014","role":"author","urls":{},"keyword":["computational complexity;hearing aids;learning (artificial intelligence);speech enhancement;speech intelligibility;Wiener filters;computationally efficient single channel speech enhancement algorithm;monaural hearing aids;supervised machine learning technique;frequency domain Wiener filter;noise reduction;intelligibility improvement;Speech;Speech enhancement;Noise measurement;Signal processing algorithms;Signal to noise ratio;Training;Speech enhancement;Noise reduction;Time-frequency masking;Supervised learning"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2014url.bib","creationDate":"2021-02-13T17:43:41.751Z","downloads":0,"keywords":["computational complexity;hearing aids;learning (artificial intelligence);speech enhancement;speech intelligibility;wiener filters;computationally efficient single channel speech enhancement algorithm;monaural hearing aids;supervised machine learning technique;frequency domain wiener filter;noise reduction;intelligibility improvement;speech;speech enhancement;noise measurement;signal processing algorithms;signal to noise ratio;training;speech enhancement;noise reduction;time-frequency masking;supervised learning"],"search_terms":["computationally","efficient","single","channel","speech","enhancement","algorithm","monaural","hearing","aids","ayllón","gil-pita","utrilla-manso","rosa-zurera"],"title":"A computationally-efficient single-channel speech enhancement algorithm for monaural hearing aids","year":2014,"dataSources":["A2ezyFL6GG6na7bbs","oZFG3eQZPXnykPgnE"]}