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
Sound Classification in Hearing Instruments
KTH, Superseded Departments, Signals, Sensors and Systems.
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 [en]
Sound Classification HMM Hearing Aid
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-3777ISBN: 91-7283-763-2 (print)OAI: oai:DiVA.org:kth-3777DiVA: diva2:9628
Public defence
2004-06-03, 00:00
Note
QC 20100611Available from: 2004-06-02 Created: 2004-06-02 Last updated: 2010-06-14Bibliographically approved
List of papers
1. The behaviour of non-linear (WDRC) hearinginstruments under realistic simulated listening conditions
Open this publication in new window or tab >>The behaviour of non-linear (WDRC) hearinginstruments under realistic simulated listening conditions
2000 (English)Report (Other academic)
Abstract [en]

This work attempts to illustrate some important practical consequences of the characteristics of nonlinear wide dynamic range compression (WDRC) hearing instruments in common conversational listening situations. Corresponding input and output signal are recorded simultaneously, using test signals consisting of conversation between a hearing aid wearer and a nonhearing aid wearer in two different listening situations, quiet and outdoors in fluctuating traffic noise. The effective insertion gain frequency response is displayed for each of the two voice sources in each of the simulated listening situations. The effective compression is also illustrated showing the gain adaptation between two alternating voice sources and the slow adaptation to changing overall acoustic conditions. These nonlinear effects are exemplified using four commercially available hearing instruments. Three of the hearing aids are digital and one is analogue.

Series
TMH-QPSR, 41, 2-3
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-13347 (URN)
Note
QC 20100614Available from: 2010-06-14 Created: 2010-06-14 Last updated: 2010-06-14Bibliographically approved
2. Hearing-aid automatic gain control adapting to two sound sources in the environment, using three time constants
Open this publication in new window or tab >>Hearing-aid automatic gain control adapting to two sound sources in the environment, using three time constants
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.

Keyword
Algorithms, Gain control, Speech recognition, Transients
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-13336 (URN)10.1121/1.1793207 (DOI)000225331500044 ()2-s2.0-9644295746 (Scopus ID)
Note
QC 20100614 QC 20110914Available from: 2010-06-14 Created: 2010-06-11 Last updated: 2011-09-14Bibliographically approved
3. An efficient robust sound classification algorithm for hearing aids
Open this publication in new window or tab >>An efficient robust sound classification algorithm for hearing aids
2004 (English)In: Journal of the Acoustical Society of America, ISSN 0001-4966, E-ISSN 1520-8524, Vol. 115, no 6, 3033-3041 p.Article in journal (Refereed) Published
Abstract [en]

An efficient robust sound classification algorithm based on hidden Markov models is presented. The system would enable a hearing aid to automatically change its behavior for differing listening environments according to the user's preferences. This work attempts to distinguish between three listening environment categories: speech in traffic noise, speech in babble, and clean speech, regardless of the signal-to-noise ratio. The classifier uses only the modulation characteristics of the signal. The classifier ignores the absolute sound pressure level and the absolute spectrum shape, resulting in an algorithm that is robust against irrelevant acoustic variations. 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 were 0.2%-1.7% in these tests. The algorithm is robust and efficient and consumes a small amount of instructions and memory. It is fully possible to implement-the classifier in a DSP-based hearing instrument.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-13337 (URN)10.1121/1.1710877 (DOI)000222153500035 ()2-s2.0-2942665634 (Scopus ID)
Note
QC 20100614Available from: 2010-06-14 Created: 2010-06-11 Last updated: 2011-10-26Bibliographically approved
4. Automatic classification of the telephone listening environment in a hearing aid
Open this publication in new window or tab >>Automatic classification of the telephone listening environment in a hearing aid
2002 (English)In: Trita-TMH / Royal Institute of Technology, Speech, Music and Hearing, ISSN 1104-5787, Vol. 43, no 1, 45-49 p.Article in journal (Refereed) Published
Abstract [en]

An algorithm is developed for automatic classification of the telephone-listening environment in a hearing instrument. The system would enable the hearing aid to automatically change its behavior when it is used for a telephone conversation (e.g., decrease the amplification in the hearing aid, or adapt the feedback suppression algorithm for reflections from the telephone handset). Two listening environments are included in the classifier. The first is a telephone conversation in quiet or in traffic noise and the second is a face-to-face conversation in quiet or in traffic. Each listening environment is modeled with two or three discrete Hidden Markov Models. The probabilities for the different listening environments are calculated with the forward algorithm for each frame of the input sound, and are compared with each other in order to detect the telephone-listening environment. The results indicate that the classifier can distinguish between the two listening environments used in the test material: telephone conversation and face-to-face conversation.

Place, publisher, year, edition, pages
Stockholm: Institutionen för tal, musik och hörsel, Tekniska högskolan i Stockholm, 2002
National Category
Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-13334 (URN)
Note
QC 20100611Available from: 2010-06-11 Created: 2010-06-11 Last updated: 2010-06-14Bibliographically approved
5. Speech Recognition in Hearing Aids
Open this publication in new window or tab >>Speech Recognition in Hearing Aids
2004 (English)In: EURASIP Journal on Wireless Communications and Networking, ISSN 1687-1472, E-ISSN 1687-1499Article in journal (Other academic) Submitted
Identifiers
urn:nbn:se:kth:diva-13338 (URN)
Note
QC 20100611Available from: 2010-06-11 Created: 2010-06-11 Last updated: 2010-06-14Bibliographically approved

Open Access in DiVA

fulltext(920 kB)1554 downloads
File information
File name FULLTEXT01.pdfFile size 920 kBChecksum MD5
f369adba23fa92c5fd2aca82006e86a54864e0946a70d3bfddb5b1b909901bb17b128f06
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Nordqvist, Peter
By organisation
Signals, Sensors and Systems
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 1554 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 717 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