Diagnosing Noise-Induced Hearing Loss Sustained During Military Service Using Deep Neural Networks

The diagnosis of noise-induced hearing loss (NIHL) is based on three requirements: a history of exposure to noise with the potential to cause hearing loss; the absence of known causes of hearing loss other than noise exposure; and the presence of certain features in the audiogram. All current methods for diagnosing NIHL have involved examination of the typical features of the audiograms of noise-exposed individuals and the formulation of quantitative rules for the identification of those features. This article describes an alternative approach based on the use of multilayer perceptrons (MLPs). The approach was applied to databases containing the ages and audiograms of individuals claiming compensation for NIHL sustained during military service (M-NIHL), who were assumed mostly to have M-NIHL, and control databases with no known exposure to intense sounds. The MLPs were trained so as to classify individuals as belonging to the exposed or control group based on their audiograms and ages, thereby automatically identifying the features of the audiogram that provide optimal classification. Two databases (noise exposed and nonexposed) were used for training and validation of the MLPs and two independent databases were used for evaluation and further analyses. The best-performing MLP was one trained to identify whether or not an individual had M-NIHL based on age and the audiogram for both ears. This achieved a sensitivity of 0.986 and a specificity of 0.902, giving an overall accuracy markedly higher than for previous methods

Overall judgment of loudness of time-varying sounds.

Listeners can judge the overall loudness of time-varying sounds quite easily, i.e., assign a single value that represents the loudness of the entire sound. This holds even if the duration is long and the judgment includes memory effects. Different metrics for calculating overall loudness have been developed. They agree that overall loudness is higher than the mean of loudness over time. Percentiles like the N5, the loudness being exceeded 5% of the time, are adopted by ISO 532-1. In the present study the concept of an energy mean known from level measurements (ISO 1996-1) was applied to the loudness domain. This equivalent continuous loudness level, LLP, was compared to the N5 using a set of real-world sounds that was orthogonal between the two metrics over a wide dynamic range of 30 phon. Cross-modality matching with line length was used in three experiments with a focus on either the overall judgment of loudness, continuous judgment while a sound was played, or both. The LLP showed considerably higher correlations with overall judgments than N5. Comparing continuous instantaneous judgment with calculated instantaneous loudness suggests that the participants might have focused on the sounds' prominent portions

The implementation of efficient hearing tests using machine learning

Time-efficient hearing tests are important in both clinical practice and research studies. Bayesian active learning (BAL) methods were first proposed in the 1990s. We developed BAL methods for measuring the audiogram, conducting notched-noise tests, determination of the edge frequency of a dead region (fe), and estimating equal-loudness contours. The methods all use a probabilistic model of the outcome, which can be classification (audible/inaudible), regression (loudness) or model parameters (fe, outer hair cell loss at fe). The stimulus parameters for the next trial (e.g. frequency, level) are chosen to yield maximum reduction in the uncertainty of the parameters of the probabilistic model. The approach reduced testing time by a factor of about 5 and, for some tests, yielded results on a continuous frequency scale. For example, auditory filter shapes can be estimated for centre frequencies from 500 to 4000 Hz in 20-30 minutes. The probabilistic modelling allows quantitative comparison of different methods. For audiogram determination, asking subjects to count the number of audible tones in a sequence with decreasing level was slightly more efficient than requiring Yes/No responses. Counting tones yielded higher variance for a single response, but this was offset by the higher information per trial

Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method.

Time-efficient hearing tests are important in both clinical practice and research studies. This particularly applies to notched-noise tests, which are rarely done in clinical practice because of the time required. Auditory-filter shapes derived from notched-noise data may be useful for diagnosis of the cause of hearing loss and for fitting of hearing aids, especially if measured over a wide range of center frequencies. To reduce the testing time, we applied Bayesian active learning (BAL) to the notched-noise test, picking the most informative stimulus parameters for each trial based on nine Gaussian Processes. A total of 11 hearing-impaired subjects were tested. In 20 to 30 min, the test provided estimates of signal threshold as a continuous function of frequency from 500 to 4000 Hz for nine notch widths and for notches placed both symmetrically and asymmetrically around the signal frequency. The thresholds were found to be consistent with those obtained using a 2-up/1-down forced-choice procedure at a single center frequency. In particular, differences in threshold between the methods did not vary with notch width. An independent second run of the BAL test for one notch width showed that it is reliable. The data derived from the BAL test were used to estimate auditory-filter width and asymmetry and detection efficiency for center frequencies from 500 to 4000 Hz. The results agreed with expectations for cochlear hearing losses that were derived from the audiogram and a hearing model

Continuous Magnitude Production of Loudness

From Frontiers via Jisc Publications RouterHistory: received 2020-11-30, collection 2021, accepted 2021-04-13, epub 2021-05-11Publication status: PublishedContinuous magnitude estimation and continuous cross-modality matching with line length can efficiently track the momentary loudness of time-varying sounds in behavioural experiments. These methods are known to be prone to systematic biases but may be checked for consistency using their counterpart, magnitude production. Thus, in Experiment 1, we performed such an evaluation for time-varying sounds. Twenty participants produced continuous cross-modality matches to assess the momentary loudness of fourteen songs by continuously adjusting the length of a line. In Experiment 2, the resulting temporal line length profile for each excerpt was played back like a video together with the given song and participants were asked to continuously adjust the volume to match the momentary line length. The recorded temporal line length profile, however, was manipulated for segments with durations between 7 to 12 s by eight factors between 0.5 and 2, corresponding to expected differences in adjusted level of −10, −6, −3, −1, 1, 3, 6, and 10 dB according to Stevens’s power law for loudness. The average adjustments 5 s after the onset of the change were −3.3, −2.4, −1.0, −0.2, 0.2, 1.4, 2.4, and 4.4 dB. Smaller adjustments than predicted by the power law are in line with magnitude-production results by Stevens and co-workers due to “regression effects.” Continuous cross-modality matches of line length turned out to be consistent with current loudness models, and by passing the consistency check with cross-modal productions, demonstrate that the method is suited to track the momentary loudness of time-varying sounds

Using a deep neural network to speed up a model of loudness for time-varying sounds

The “time-varying loudness (TVL)” model calculates “instantaneous loudness” every 1 ms, and this is used to generate predictions of short-term loudness, the loudness of a short segment of sound such as a word in a sentence, and of long-term loudness, the loudness of a longer segment of sound, such as a whole sentence. The calculation of instantaneous loudness is computationally intensive and real-time implementation of the TVL model is difficult. To speed up the computation, a deep neural network (DNN) has been trained to predict instantaneous loudness using a large database of speech sounds and artificial sounds (tones alone and tones in white or pink noise), with the predictions of the TVL model as a reference (providing the "correct" answer, specifically the loudness level in phons). A multilayer perceptron with three hidden layers was found to be sufficient, with more complex DNN architecture not yielding higher accuracy. After training, the deviations between the predictions of the TVL model and the predictions of the DNN were typically less than 0.5 phons, even for types of sounds that were not used for training (music, rain, animal sounds, washing machine). The DNN calculates instantaneous loudness over 100 times more quickly than the TVL model

Development of a Deep Neural Network for Speeding Up a Model of Loudness for Time-Varying Sounds

The “time-varying loudness” (TVL) model of Glasberg and Moore calculates “instantaneous loudness” every 1 ms, and this is used to generate predictions of short-term loudness, the loudness of a short segment of sound, such as a word in a sentence, and of long-term loudness, the loudness of a longer segment of sound, such as a whole sentence. The calculation of instantaneous loudness is computationally intensive and real-time implementation of the TVL model is difficult. To speed up the computation, a deep neural network (DNN) was trained to predict instantaneous loudness using a large database of speech sounds and artificial sounds (tones alone and tones in white or pink noise), with the predictions of the TVL model as a reference (providing the “correct” answer, specifically the loudness level in phons). A multilayer perceptron with three hidden layers was found to be sufficient, with more complex DNN architecture not yielding higher accuracy. After training, the deviations between the predictions of the TVL model and the predictions of the DNN were typically less than 0.5 phons, even for types of sounds that were not used for training (music, rain, animal sounds, and washing machine). The DNN calculates instantaneous loudness over 100 times more quickly than the TVL model. Possible applications of the DNN are discussed