5aPA – A Robust Smartphone Based Multi-Channel Dynamic-Range Audio Compression for Hearing Aids

Yiya Hao– yxh133130@utdallas.edu
Ziyan Zou – ziyan.zou@utdallas.edu
Dr. Issa M S Panahi – imp015000@utdallas.edu

Statistical Signal Processing Laboratory (SSPRL)
The University of Texas at Dallas
800W Campbell Road, Richardson, TX – 75080, USA

Popular Version of Paper 5aPA, “A Robust Smartphone Based Multi-Channel Dynamic-Range Audio Compression for Hearing Aids”
Presented Friday morning, May 11, 2018, 10:15 – 10:30 AM, GREENWAY J
175th ASA Meeting, Minneapolis

Records by National Institute on Deafness and Other Communication Disorders (NIDCD) indicate that nearly 15% of adults (37 million) aged 18 and over report some kind of hearing loss in the United States. Amongst the entire world population, 360 million people suffer from hearing loss.

Hearing impairment degrades perception of speech and audio signals due to low frequency- dependent audible threshold levels. Hearing aid devices (HADs) apply prescription gains and dynamic-range compression for improving users’ audibility without increasing the sound loudness to uncomfortable levels. Multi-Channel dynamic-range compression enhances quality and intelligibility of audio output by targeting each frequency band with different compression parameters such as compression ratio (CR), attack time (AT) and release time (RT).

Increasing the number of compression channels can result in more comfortable audio output when appropriate parameters are defined for each channel. However, the use of more channels increases computational complexity of the multi-channel compression algorithm limiting its application to some HADs. In this paper, we propose a nine-channel dynamic-range compression (DRC) with an optimized structure capable of running on smartphones and other portable digital platforms in real time. Test results showing the performance of proposed method are presented too. The block diagram of proposed method shows in Fig.1. And the block diagram of compressor shows in the Fig.2.

Fig.1. Block Diagram of 9-Channel Dynamic-Range Audio Compression

Fig.2. Block Diagram of Compressor

Several experimental results have been measured including the processing time measurements of real-time implementation of proposed method on an Android smartphone, objective evaluations and subjective evaluations, a commercial audio compression & limiter provided by Hotto Engineering [1] is used as a comparison running on a laptop. Proposed method running on a Google Pixel smartphone with operating system 6.0.1. The sampling rate is set to 16kHz and the frame size is set as 10 ms.

The High-quality INT eractomes (HINT) sentences database at 16 kHz sampling rate are used. First experimental measurement is testing the processing time running on the smartphone. Two processing times were measured, round-trip latency and algorithms processing time. Larsen test was used to measure the round-trip latency [2], and the test setup shows in Fig.3. The average processing time results shows in Fig.2 as well. Perceptual evaluation of speech quality (PESQ) [3] and short-time objective intelligibility (STOI) [4] has been used to test the objective quality and intelligibility of proposed nine-channel DRC.

The results could be find in Fig.4. Subjective tests including mean opinion score (MOS) test [5] and word recognition test (WR) have been tested, and the Fig.5 shows the results. Based on the results we can tell that proposed nine-channel DRC could run on the smartphone efficiently, and provides with decent quality and intelligibility as well.

Fig.3. Processing Time Measurements and Results

Fig.4. Objective evaluation results of speech quality and intelligibility.

Fig.5. Subjective evaluation results of speech quality and intelligibility.

Based on the results we can tell, proposed nine-channel dynamic-range audio compression could provide with decent the quality and intelligibility which could run on smartphones. Proposed DRC could pre-set all the parameters based on the audiograms of individuals. With proposed compression, the multi-channel DRC does not limit within advanced hardware, which is costly such as hearing aids or laptops. Proposed method also provides with a portable audio framework, which not just limiting in current version of DRC, but could be extended or upgraded further for research study.

Please refer our lab website http://www.utdallas.edu/ssprl/hearing-aid-project/ for video demos and the sample audio files are as attached below.

Audio files:







Key References:

  • 2018. [Online]. Available: http://www.hotto.de/
  • 2018. [Online]. Available: https://source.android.com/devices/audio/latency_measurements
  • Rix, W., J. G. Beerends J.G., Hollier, M. P., Hekstra, A. P., “Perceptual evaluation of speech quality (PESQ) – a new method for speech quality assessment of telephone networks and codecs,” IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), 2, pp. 749-752., May 2001.
  • Tall, C. H, Hendricks, R. C., Heusdens, R., Jensen, R., “An algorithm for intelligibility prediction of time-frequency weighted noisy speech,” IEEE trans. Audio, Speech, Lang. Process. 19(7), pp. 2125- 2136., Feb
  • Streijl, R. C., Winkler, S., Hands, D. S., “Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives,” in Multimedia Systems 22.2, pp. 213-227, 2016.

*This work was supported by the National Institute of the Deafness and Other Communication Disorders (NIDCD) of the National Institutes of Health (NIH) under the grant number 5R01DC015430-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors are with the Statistical Signal Processing Research Laboratory (SSPRL), Department of Electrical and Computer Engineering, The University of Texas at Dallas.

2pNSb – A smartphone noise meter app in every pocket?

Chucri A. Kardous – ckardous@cdc.gov
Peter B. Shaw – pbs3@cdc.gov
National Institute for Occupational Safety and Health
Centers for Disease Control and Prevention
1090 Tusculum Avenue
Cincinnati, Ohio 45226

Popular version of paper 2pNSb, “Use of smartphone sound measurement apps for occupational noise assessments”
Presented Tuesday May 19, 2015, 3:55 PM, Ballroom 1
169th ASA Meeting, Pittsburgh, PA
See also: Evaluation of smartphone sound measurement applications

Our world is getting louder. Excessive noise is a public health problem and can cause a range of health issues; noise exposure can induce hearing impairment, cardiovascular disease, hypertension, sleep disturbance, and a host of other psychological and social behavior problems. The World Health Organization (WHO) estimates that there are 360 million people with disabling hearing loss. Occupational hearing loss is the most common work-related illness in the United States; the National Institute for Occupational Safety and Health (NIOSH) estimates that approximately 22 million U.S. workers are exposed to hazardous noise.

Smartphones users are expected to hit the 2 billion mark in 2015. The ubiquity of smartphones and the sophistication of current sound measurement applications (apps) present a great opportunity to revolutionize the way we look at noise and its effects on our hearing and overall health. Through the use of crowdsourcing techniques, people around the world may be able to collect and share noise exposure data using their smartphones. Scientists and public health professionals could rely on such shared data to promote better hearing health and prevention efforts. In addition, the ability to acquire and display real-time noise exposure data raises people’s awareness about their work (and off-work) environment and allows them to make informed decisions about hazards to their hearing and overall well-being. For instance, the European Environment Agency (EEA) developed the Noise Watch app that allows citizens around the world to make noise measurements whether at their work or during their leisure activities, and upload that data to a database in real time and using the smartphone GPS capabilities to construct a map of the noisiest places and sources in their environment.

However, not all smartphone sound measurements apps are equal. Some are basic and not very accurate while some are much more sophisticated. NIOSH researchers conducted a study of 192 smartphone sound measurement apps to examine the accuracy and functionality of such apps. We conducted the study in our acoustics laboratory and compared the results to a professional sound level meter. Only 10 apps met our selection criteria, and of those only 4 met our accuracy requirements of being within ±2 decibels (dB) of type 1 professional sound level meter. Apps developed for the iOS platform were more advanced, functionality and performance wise, than Android apps. You can read more about our original study on our NIOSH Science Blog at: http://blogs.cdc.gov/niosh-science-blog/2014/04/09/sound-apps/ or download our JASA paper at: http://scitation.aip.org/content/asa/journal/jasa/135/4/10.1121/1.4865269.

Testing the SoundMeter app on the iPhone 5 and iPhone 4S
Figure 1. Testing the SoundMeter app on the iPhone 5 and iPhone 4S against a ½” Larson-Davis 2559 random incidence reference microphone
Today, we will present on our additional efforts to examine the accuracy of smartphone sound measurement apps using external microphones that can be calibrated. There are several external microphones available mostly for the consumer market, and although they vary greatly in price, they all possess similar acoustical specifications and have performed similarly in our laboratory tests. Preliminary results showed even greater agreement with professional sound measurement instruments (± 1 dB) over our testing range.

Calibrating the SPLnFFT app
Figure 2. Calibrating the SPLnFFT app with MicW i436 external microphone using the Larson-Davis CAL250 acoustic calibrator (114 dB SPL @ 250Hz)

Figure 3

Figure 3. Laboratory testing of 4 iOS devices using MicW i436 and comparing the measurements to a Larson-Davis type 831 sound level meter (pink noise at 75 dBA)

We will also discuss our plans to develop and distribute a free NIOSH Sound Level Meter app in an effort to facilitate future occupational research efforts and build an noise job exposure database.

Challenges remain with using smartphones to collect and document noise exposure data. Some of the main issues encountered in recent studies relate to privacy and collection of personal data, sustained motivation to participate in such studies, bad or corrupted data, and mechanisms for storing and accessing such data.