Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Hearing-aid automatic gain control adapting to two sound sources in the environment, using three time constants
KTH, Superseded Departments, Speech, Music and Hearing.
KTH, Superseded Departments, Speech, Music and Hearing.
2004 (English)In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 116, no 5, 3152-3155 p.Article in journal (Refereed) Published
Abstract [en]

A hearing aid AGC algorithm is presented that uses a richer representation of the sound environment than previous algorithms. The proposed algorithm is designed to (1) adapt slowly (in approximately 10 s) between different listening environments, e.g., when the user leaves a single talker lecture for a multi-babble coffee-break; (2) switch rapidly (about 100 ms) between different dominant sound sources within one listening situation, such as the change from the user's own voice to a distant speaker's voice in a quiet conference room; (3) instantly reduce gain for strong transient sounds and then quickly return to the previous gain setting; and (4) not change the gain in silent pauses but instead keep the gain setting of the previous sound source. An acoustic evaluation showed that the algorithm worked as intended. The algorithm was evaluated together with a reference algorithm in 4 pilot field test. When evaluated by nine users in a set of speech recognition tests, the algorithm showed similar results to the reference algorithm.

Place, publisher, year, edition, pages
2004. Vol. 116, no 5, 3152-3155 p.
Keyword [en]
Algorithms, Gain control, Speech recognition, Transients
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-13336DOI: 10.1121/1.1793207ISI: 000225331500044Scopus ID: 2-s2.0-9644295746OAI: oai:DiVA.org:kth-13336DiVA: diva2:324128
Note
QC 20100614 QC 20110914Available from: 2010-06-14 Created: 2010-06-11 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Sound Classification in Hearing Instruments
Open this publication in new window or tab >>Sound Classification in Hearing Instruments
2004 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

A variety of algorithms intended for the new generation of hearing aids is presented in this thesis. The main contribution of this work is the hidden Markov model (HMM) approach to classifying listening environments. This method is efficient and robust and well suited for hearing aid applications. This thesis shows that several advanced classification methods can be implemented in digital hearing aids with reasonable requirements on memory and calculation resources.

A method for analyzing complex hearing aid algorithms is presented. Data from each hearing aid and listening environment is displayed in three different forms: (1) Effective temporal characteristics (Gain-Time), (2) Effective compression characteristics (Input-Output), and (3) Effective frequency response (Insertion Gain). The method works as intended. Changes in the behavior of a hearing aid can be seen under realistic listening conditions. It is possible that the proposed method of analyzing hearing instruments generates too much information for the user.

An automatic gain controlled (AGC) hearing aid algorithm adapting to two sound sources in the listening environment is presented. The main idea of this algorithm is to: (1) adapt slowly (in approximately 10 seconds) to varying listening environments, e.g. when the user leaves a disciplined conference for a multi-babble coffee-break; (2) switch rapidly(in about 100 ms) between different dominant sound sources within one listening situation, such as the change from the user's own voice to a distant speaker's voice in a quiet conference room; (3) instantly reduce gain for strong transient sounds and then quickly return to the previous gain setting; and (4) not change the gain in silent pauses but instead keep the gain setting of the previous sound source. An acoustic evaluation shows that the algorithm works as intended.

A system for listening environment classification in hearing aids is also presented. The task is to automatically classify three different listening environments: 'speech in quiet', 'speech in traffic', and 'speech in babble'. The study shows that the three listening environments can be robustly classified at a variety of signal-to-noise ratios with only a small set of pre-trained source HMMs. The measured classification hit rate was 96.7-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False alarm rates were0.2-1.7% in these tests. The study also shows that the system can be implemented with the available resources in today's digital hearing aids. Another implementation of the classifier shows that it is possible to automatically detect when the person wearing the hearing aid uses the telephone. It is demonstrated that future hearing aids may be able to distinguish between the sound of a face-to-face conversation and a telephone conversation, both in noisy and quiet surroundings. However, this classification algorithm alone may not be fast enough to prevent initial feedback problems when the user places the telephone handset at the ear.

A method using the classifier result for estimating signal and noise spectra for different listening environments is presented. This evaluation shows that it is possible to robustly estimate signal and noise spectra given that the classifier has good performance.

An implementation and an evaluation of a single keyword recognizer for a hearing instrument are presented. The performance for the best parameter setting gives 7e-5 [1/s] in false alarm rate, i.e. one false alarm for every four hours of continuous speech from the user, 100% hit rate for an indoors quiet environment, 71% hit rate for an outdoors/traffic environment and 50% hit rate for a babble noise environment. The memory resource needed for the implemented system is estimated to 1820 words (16-bits). Optimization of the algorithm together with improved technology will inevitably make it possible to implement the system in a digital hearing aid within the next couple of years. A solution to extend the number of keywords and integrate the system with a sound environment classifier is also outlined.

Place, publisher, year, edition, pages
Stockholm: KTH, 2004. 51 p.
Series
Trita-S3-SIP, ISSN 1652-4500 ; 2004:2
Keyword
Sound Classification HMM Hearing Aid
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-3777 (URN)91-7283-763-2 (ISBN)
Public defence
2004-06-03, 00:00
Note
QC 20100611Available from: 2004-06-02 Created: 2004-06-02 Last updated: 2010-06-14Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Nordqvist, PeterLeijon, Arne
By organisation
Speech, Music and Hearing
In the same journal
Journal of the Acoustical Society of America
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 92 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf