Abstract
In hearing aids, noise suppression algorithms that rely on spatial cues tend to improve the intelligibility of speech in noisy environments [1], [2], [3], [4]. Unfortunately, the location of target and noise sources can change rapidly in a natural, everyday acoustic environment. In fact, depending on what the listener is attending to, one source may be considered noise in one instant, and then considered the target in another instant. Adaptive filtering attempts to track the target source, but it is successful only under a set of simplifying constraints [5], [6]. It is much more effective to allow the user to determine the direction from which the target sound is coming.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jang, H., Song, J., Jeong, H.: Advanced narrow speech channeling algorithm for robot speech recognition. In: Proceedings of the 2009 International Conference on Hybrid Information Technology, pp. 130–137. ACM (2009)
Gravel, J.S., Fausel, N., Liskow, C., Chobot, J.: Children’s speech recognition in noise using omni-directional and dual-microphone hearing aid technology. Ear and Hearing 20(1), 1–11 (1999)
Chung, K., Zeng, F.-G., Acker, K.N.: Effects of directional microphone and adaptive multichannel noise reduction algorithm on cochlear implant performance. The Journal of the Acoustical Society of America 120(4), 2216–2227 (2006)
Ricketts, T., Galster, J., Tharpe, A.M.: Directional benefit in simulated classroom environments. American Journal of Audiology 16(2), 130–144 (2007)
Wang, D.: Time-frequency masking for speech separation and its potential for hearing aid design. Trends in Amplification 12(4), 332–353 (2008)
Boldt, J., Kjems, U., Pedersen, M.S., Lunner, T., Wang, D.: Estimation of the ideal binary mask using directional systems. In: International Workshop on Acoustic Echo and Noise Control, IWAENC 2008 (2008)
Goetze, S., Rohdenburg, T., Hohmann, V., Kollmeier, B., Kammeyer, K.-D.: Direction of arrival estimation based on the dual delay line approach for binaural hearing aid microphone arrays. In: International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2007, pp. 84–87. IEEE (2007)
Shinn-Cunningham, B.G., Best, V.: Selective attention in normal and impaired hearing. Trends in Amplification 12(4), 283–299 (2008)
Morla, A.: Four transformative patient demands: convenience, size, simplicity, and flexibility. Hearing Review 18(4), 36–42 (2011)
Witt, H., Nicolai, T., Kenn, H.: Designing a wearable user interface for hands-free interaction in maintenance applications. In: PerCom Workshops, pp. 652–655 (2006)
Oulasvirta, A., Bergstrom-Lehtovirta, J.: Ease of juggling: studying the effects of manual multitasking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3103–3112. ACM (2011)
Ng, A., Brewster, S.A., Williamson, J.: The impact of encumbrance on mobile interactions. In: Kotzé, P., Marsden, G., Lindgaard, G., Wesson, J., Winckler, M. (eds.) INTERACT 2013, Part III. LNCS, vol. 8119, pp. 92–109. Springer, Heidelberg (2013)
D’albis, T., Blatt, R., Tedesco, R., Sbattella, L., Matteucci, M.: A predictive speller controlled by a brain-computer interface based on motor imagery. ACM Transactions on Computer-Human Interaction (TOCHI) 19(3), 20 (2012)
Krusienski, D.J., Sellers, E.W., Cabestaing, F., Bayoudh, S., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A comparison of classification techniques for the p300 speller. Journal of Neural Engineering 3(4), 299 (2006)
Nijboer, F., Sellers, E.W., Mellinger, J., Jordan, M.A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D.J., Vaughan, T.M., et al.: A p300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology 119(8), 1909–1916 (2008)
Iturrate, I., Antelis, J.M., Kubler, A., Minguez, J.: A noninvasive brain-actuated wheelchair based on a p300 neurophysiological protocol and automated navigation. IEEE Transactions on Robotics 25(3), 614–627 (2009)
Gao, S., Wang, Y., Gao, X., Hong, B.: Visual and auditory brain-computer interfaces. IEEE Transactions on Biomedical Engineering 61(5), 1436–1447 (2014)
Lopez-Gordo, M.A., Fernandez, E., Romero, S., Pelayo, F., Prieto, A.: An auditory brain–computer interface evoked by natural speech. Journal of Neural Engineering 9(3), 036013 (2012)
Klobassa, D.S., Vaughan, T.M., Brunner, P., Schwartz, N.E., Wolpaw, J.R., Neuper, C., Sellers, E.W.: Toward a high-throughput auditory p300-based brain–computer interface. Clinical Neurophysiology 120(7), 1252–1261 (2009)
Guo, J., Gao, S., Hong, B.: An auditory brain–computer interface using active mental response. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18(3), 230–235 (2010)
Höller, Y., Kronbichler, M., Bergmann, J., Crone, J.S., Ladurner, G., Golaszewski, S.: Eeg frequency analysis of responses to the own-name stimulus. Clinical Neurophysiology 122(1), 99–106 (2011)
Hanson, V., Odame, K.: Real-time embedded implementation of the binary mask algorithm for hearing prosthetics. IEEE Transactions on Biomedical Circuits and Systems (2013)
Polich, J.: Habituation of p300 from auditory stimuli. Psychobiology 17(1), 19–28 (1989)
O’Sullivan, J.A., Power, A.J., Mesgarani, N., Rajaram, S., Foxe, J.J., Shinn-Cunningham, B.G., Slaney, M., Shamma, S.A., Lalor, E.C.: Attentional selection in a cocktail party environment can be decoded from single-trial eeg. Cerebral Cortex, bht355 (2014)
Ding, N., Simon, J.Z.: Emergence of neural encoding of auditory objects while listening to competing speakers. Proceedings of the National Academy of Sciences 109(29), 11854–11859 (2012)
Lunner, T.: Method of operating a hearing instrument based on an estimation of present cognitive load of a user and a hearing aid system. US Patent App. 12/642,345 (December 18, 2009)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Transactions on Biomedical Engineering 51(6), 1034–1043 (2004)
Daubigney, L., Pietquin, O., et al.: Single-trial p300 detection with kalman ltering and svms. In: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 399–404 (2011)
Mirghasemi, H., Fazel-Rezai, R., Shamsollahi, M.B.: Analysis of p300 classifiers in brain computer interface speller. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, pp. 6205–6208. IEEE (2006)
Meng, H., Freeman, M., Pears, N., Bailey, C.: Real-time human action recognition on an embedded, reconfigurable video processing architecture. Journal of Real-Time Image Processing 3(3), 163–176 (2008)
Meng, H., Pears, N., Freeman, M., Bailey, C.: Motion history histograms for human action recognition. In: Embedded Computer Vision, pp. 139–162. Springer (2009)
Oostenveld, R., Praamstra, P.: The five percent electrode system for high-resolution eeg and erp measurements. Clinical Neurophysiology 112(4), 713–719 (2001)
Garofolo, J.S., et al.: TIMIT: acoustic-phonetic continuous speech corpus. Linguistic Data Consortium (1993)
Clinton, H.: Remarks on american leadership. Council on Foreign Relations (January 31, 2013)
Tillerson, R.: The new north american energy paradigm: Reshaping the future, Council on Foreign Relations (June 27, 2012)
Perelmouter, J., Birbaumer, N.: A binary spelling interface with random errors. IEEE Transactions on Rehabilitation Engineering 8(2), 227–232 (2000)
Johnson, R.: On the neural generators of the p300 component of the event-related potential. Psychophysiology 30(1), 90–97 (1993)
Polich, J.: Updating p300: an integrative theory of p3a and p3b. Clinical Neurophysiology 118(10), 2128–2148 (2007)
Stuermann, B.: Auto zoomcontrol-objective and subjective benefits with auto zoomcontrol. Phonak Field Study News (2011)
Jang, G.-J., Lee, T.-W.: A maximum likelihood approach to single-channel source separation. The Journal of Machine Learning Research 4, 1365–1392 (2003)
Davies, M.E., James, C.J.: Source separation using single channel ica. Signal Processing 87(8), 1819–1832 (2007)
Lunner, T., Neher, T.: Automatic real-time hearing aid fitting based on auditory evoked potentials. US Patent App. 13/651,032 (October 12, 2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Hanson, V., Odame, K. (2015). Towards a Brain-Machine System for Auditory Scene Analysis. In: Mukhopadhyay, S. (eds) Wearable Electronics Sensors. Smart Sensors, Measurement and Instrumentation, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-18191-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-18191-2_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18190-5
Online ISBN: 978-3-319-18191-2
eBook Packages: EngineeringEngineering (R0)