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Automatic classification of the telephone listening environment in a hearing aid
KTH, Tidigare Institutioner                               , Signaler, sensorer och system.
KTH, Tidigare Institutioner                               , Signaler, sensorer och system.
2002 (engelsk)Inngår i: Trita-TMH / Royal Institute of Technology, Speech, Music and Hearing, ISSN 1104-5787, Vol. 43, nr 1, s. 45-49Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Stockholm: Institutionen för tal, musik och hörsel, Tekniska högskolan i Stockholm , 2002. Vol. 43, nr 1, s. 45-49
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-13334OAI: oai:DiVA.org:kth-13334DiVA, id: diva2:323737
Merknad
QC 20100611Tilgjengelig fra: 2010-06-11 Laget: 2010-06-11 Sist oppdatert: 2018-01-12bibliografisk kontrollert
Inngår i avhandling
1. Sound Classification in Hearing Instruments
Åpne denne publikasjonen i ny fane eller vindu >>Sound Classification in Hearing Instruments
2004 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH, 2004. s. 51
Serie
Trita-S3-SIP, ISSN 1652-4500 ; 2004:2
Emneord
Sound Classification HMM Hearing Aid
HSV kategori
Identifikatorer
urn:nbn:se:kth:diva-3777 (URN)91-7283-763-2 (ISBN)
Disputas
2004-06-03, 00:00
Merknad
QC 20100611Tilgjengelig fra: 2004-06-02 Laget: 2004-06-02 Sist oppdatert: 2010-06-14bibliografisk kontrollert

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