The Impact of Pitch and Timbre Cues on Auditory Grouping and Stream Segregation

In multi-talker listening environments, the culmination of different voice streams may lead to the distortion of each source’s individual message, causing deficits in comprehension. Voice characteristics, such as pitch and timbre, are major dimensions of auditory perception and play a vital role in grouping and segregating incoming sounds based on their acoustic properties. The current study investigated how pitch and timbre cues (determined by fundamental frequency, notated as F0, and spectral slope, respectively) can affect perceptual integration and segregation of complex-tone sequences within an auditory streaming paradigm. Twenty normal-hearing listeners participated in a traditional auditory streaming experiment using two alternating sequences of harmonic tone complexes A and B with manipulating F0 and spectral slope. Grouping ranges, the F0/spectral slope ranges over which auditory grouping occurs, were measured with various F0/spectral slope differences between tones A and B. Results demonstrated that the grouping ranges were maximized in the absence of the F0/spectral slope differences between tones A and B and decreased by 2 times as their differences increased to ±1-semitone F0 and ±1-dB/octave spectral slope. In other words, increased differences in either F0 or spectral slope allowed listeners to more easily distinguish between harmonic stimuli, and thus group them together less. These findings suggest that pitch/timbre difference cues play an important role in how we perceive harmonic sounds in an auditory stream, representing our ability to group or segregate human voices in a multi-talker listening environment.

Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans

Understanding speech becomes a demanding task when the environment is noisy. Comprehension of speech in noise can be substantially improved by looking at the speaker’s face, and this audiovisual benefit is even more pronounced in people with hearing impairment. Recent advances in AI have allowed to synthesize photorealistic talking faces from a speech recording and a still image of a person’s face in an end-to-end manner. However, it has remained unknown whether such facial animations improve speech-in-noise comprehension. Here we consider facial animations produced by a recently introduced generative adversarial network (GAN), and show that humans cannot distinguish between the synthesized and the natural videos. Importantly, we then show that the end-to-end synthesized videos significantly aid humans in understanding speech in noise, although the natural facial motions yield a yet higher audiovisual benefit. We further find that an audiovisual speech recognizer (AVSR) benefits from the synthesized facial animations as well. Our results suggest that synthesizing facial motions from speech can be used to aid speech comprehension in difficult listening environments.

Speech-driven Facial Animations Improve Speech-in-Noise Comprehension of Humans

Understanding speech becomes a demanding task when the environment is noisy. Comprehension of speech in noise can be substantially improved by looking at the speake's face, and this audiovisual benefit is even more pronounced in people with hearing impairment. Recent advances in AI have allowed to synthesize photorealistic talking faces from a speech recording and a still image of a person's face in an end-to-end manner. However, it has remained unknown whether such facial animations improve speech-in-noise comprehension. Here we consider facial animations produced by a recently introduced generative adversarial network (GAN), and show that humans cannot distinguish between the synthesized and the natural videos. Importantly, we then show that the end-to-end synthesized videos significantly aid humans in understanding speech in noise, although the natural facial motions yield a yet higher audiovisual benefit. We further find that an audiovisual speech recognizer benefits from the synthesized facial animations as well. Our results suggest that synthesizing facial motions from speech can be used to aid speech comprehension in difficult listening environments.

Music perception and speech intelligibility in noise performance by Italian-speaking cochlear implant users

Objective The goal of this study was to investigate the performance correlations between music perception and speech intelligibility in noise by Italian-speaking cochlear implant (CI) users. Materials and methods Twenty postlingually deafened adults with unilateral CIs (mean age 65 years, range 46–92 years) were tested with a music quality questionnaire using three passages of music from Classical Music, Jazz, and Soul. Speech recognition in noise was assessed using two newly developed adaptive tests in Italian: The Sentence Test with Adaptive Randomized Roving levels (STARR) and Matrix tests. Results Median quality ratings for Classical, Jazz and Soul music were 63%, 58% and 58%, respectively. Median SRTs for the STARR and Matrix tests were 14.3 dB and 7.6 dB, respectively. STARR performance was significantly correlated with Classical music ratings (rs = − 0.49, p = 0.029), whereas Matrix performance was significantly correlated with both Classical (rs = − 0.48, p = 0.031) and Jazz music ratings (rs = − 0.56, p = 0.011). Conclusion Speech with competitive noise and music are naturally present in everyday listening environments. Recent speech perception tests based on an adaptive paradigm and sentence materials in relation with music quality measures might be representative of everyday performance in CI users. The present data contribute to cross-language studies and suggest that improving music perception in CI users may yield everyday benefit in speech perception in noise and may hence enhance the quality of listening for CI users.

TAʿLĪM AL-LUĠAH AL-ʿARABIYYAH FĪ AL-MAʿĀHID AL-ISLĀMIYYAH FĪ INDŪNĪSIYYĀ

This study aims to describe the academic material in Arabic teaching in the salaf and the modern Institutions, and describe the similarities and differences of Arabic teaching in both of them.The study used a qualitative. In this study the data were collected by using interviews, observation, and documentation. To analyze the data, the researcher used the interactive technique of Miles and Huberman, while to test the validity of the data, the method of triangulation technique was used.The result of the study shows the following: (1) The Arabic teaching materials in Rasyidiyah Khalidiyah Islamic boarding schools consist of 10 subjects, while in the Raudhatut Thalibin only consist of 5 subjects. The Rasyidiyah Khalidiyah boarding schools use theory of integrated and separated system in their Arabic teaching materials, while the Raudhatut Thalibin use separated system theory; (2) The instructional method engaged in Arabic teaching is the eclectic method. The teaching use concrete (indrawi) media. Both oral and written test were applied in the evaluation system; (3) The kinds of language environment applied in Arabic teaching in Rasyidiyah Kalidiyah Islamic boarding school consist of the environment of listening and reading. While speaking, reading and listening environments, were applied in Raudhatut Thalibin Islamic boarding school.Keywor

Environmental Classification in Hearing Aids

There are two parts to this article. The first is a general overview of how hearing aid classification works, including a comparison study of normal-hearing listeners and multiple manufacturers' hearing aids while listening to a sound parkour composed of a multitude of acoustic scenes. Most hearing aids applied nearly identical classification for simple listening environments. But differences began to appear across manufacturers' products when the listening environments became more complex. The second section reviews the results of a study of the acoustic ecology (listening environments) experienced by several cohorts of hearing aid users over a 4-month period. The percentages of time people spent in seven different listening environments were mapped. It was learned that they spent an average of 57% of their time in conversation and that age is not a good predictor of the amount of time spent in most listening environments. This is because, when grouped by age, there was little to no difference in the distribution of time spent in the seven listening environments, whereas there was tremendous variability within each age group.

The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound

Hearing aid gain and signal processing are based on assumptions about the average user in the average listening environment, but problems may arise when the individual hearing aid user differs from these assumptions in general or specific ways. This article describes how an artificial intelligence (AI) mechanism that operates continuously on input from the user may alleviate such problems by using a type of machine learning known as Bayesian optimization. The basic AI mechanism is described, and studies showing its effects both in the laboratory and in the field are summarized. A crucial fact about the use of this AI is that it generates large amounts of user data that serve as input for scientific understanding as well as for the development of hearing aids and hearing care. Analyses of users' listening environments based on these data show the distribution of activities and intentions in situations where hearing is challenging. Finally, this article demonstrates how further AI-based analyses of the data can drive development.

Creating Clarity in Noisy Environments by Using Deep Learning in Hearing Aids

Hearing aids continue to acquire increasingly sophisticated sound-processing features beyond basic amplification. On the one hand, these have the potential to add user benefit and allow for personalization. On the other hand, if such features are to benefit according to their potential, they require clinicians to be acquainted with both the underlying technologies and the specific fitting handles made available by the individual hearing aid manufacturers. Ensuring benefit from hearing aids in typical daily listening environments requires that the hearing aids handle sounds that interfere with communication, generically referred to as “noise.” With this aim, considerable efforts from both academia and industry have led to increasingly advanced algorithms that handle noise, typically using the principles of directional processing and postfiltering. This article provides an overview of the techniques used for noise reduction in modern hearing aids. First, classical techniques are covered as they are used in modern hearing aids. The discussion then shifts to how deep learning, a subfield of artificial intelligence, provides a radically different way of solving the noise problem. Finally, the results of several experiments are used to showcase the benefits of recent algorithmic advances in terms of signal-to-noise ratio, speech intelligibility, selective attention, and listening effort.

Effect of Hearing Aid Technology Level and Individual Characteristics on Listener Outcome Measures

Purpose The purpose of the study was to determine the effect of hearing aid technology level on listener outcome measures. In addition, we aimed to determine if individual characteristics such as noise acceptance and the demands of the listening environment impacted performance and preference. Method A repeated-measures, single-blinded research design was utilized. Twenty-four adults recruited by mail from The University of Tennessee Health Science Center Audiology Clinic participated in this experiment (15 men and nine women). Participants completed two 2-week trial periods using Unitron T Moxi Fit FLEX:TRIAL devices programmed as basic or premium technology levels. A data-logging feature, Log It All (LIA), quantified the demands of the listening environment. At the end of each trial, outcome measures were obtained using Pascoe's High-Frequency Word List, the Hearing in Noise Test, the Quick Speech-in-Noise Test, the Acceptable Noise Level (ANL), the Speech, Spatial and Qualities of Hearing short form, satisfaction ratings, and preference. Results Results for ANL, satisfaction in large groups, and LIA total coverage were significantly improved for the premium devices. Participants who preferred the premium devices received significant improvement with premium devices on the ANL and the speech in small group and speech in large group satisfaction ratings, whereas participants who preferred the basic devices did not receive significant improvement with premium devices on any outcome measure. Participants in more demanding listening environments received significant improvement with premium devices on the ANL, whereas participants in less demanding listening environments did not receive significant improvement with premium devices on any outcome measure. Conclusions Group data revealed similar outcomes between technology levels on most measures; however, noise acceptance and satisfaction for speech in a large group were significantly improved when using the premium devices. Individual characteristics such as noise acceptance and listening demands may be useful when comparing hearing aid technology levels for a given patient.