–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Ghosts in Online Meetings: Why Clear Voices Sometimes Get Lost Have you ever noticed that voices suddenly sound unclear during an online meeting—even though the speaker believes they are speaking clearly? You may find yourself straining to listen, missing words, or misunderstanding what was said. These problems are surprisingly common and can be difficult to fix on the spot, especially when meeting participants are not familiar with the technical details of videoconference systems.
We study this hidden problem by developing a machine learning–based system that can evaluate speech quality without interrupting the meeting. Our goal is to detect sound problems automatically, before they become frustrating for listeners.
AI Transcription vs. Human Listening Humans are remarkably good at understanding speech, even when parts of it are missing or covered by noise. When a word is unclear, listeners often guess the meaning from context and still understand the overall message.
Automatic speech transcription, which is now widely used to record and summarize meetings, works very differently. AI systems analyze sound exactly as it is received. If speech is distorted, masked by noise, or partially missing, transcription accuracy drops sharply.
We turn this weakness into a strength. By measuring how much transcription quality degrades, we use AI transcription accuracy as an indicator of speech quality. In other words, if the transcription struggles, listeners are likely struggling too.
Causes of sound deterioration Sound deterioration during online meetings can be grouped into four main causes (Figure 1):
Speech factors
How and what the speaker says, such as speaking speed or clarity.
Acoustic factors
Background noise or room reverberation that affects sound before it reaches the microphone.
System factors
Problems with microphones, cables, or audio hardware quality.
Communication factors
Network issues that occur after sound is converted into digital data, such as data compression or packet loss.
Our research focuses on communication factors, which are especially important in videoconference systems and differ from traditional phone calls.
Figure 1. Causes of sound deterioration
Packet loss simulation Online meetings send sound over the internet in small pieces called packets. We use the SILK audio codec, a common system for converting speech into a format suitable for network transmission. Sometimes, these packets are lost during transmission, causing brief gaps or distortions in the sound.
To study this effect, we intentionally simulate packet loss and create artificially degraded speech. This allows us to generate large amounts of training data and teach machine learning models what poor communication quality sounds like.
Figures 2 and 3 compare a clean speech signal with a packet-loss-simulated version, showing how missing data changes the sound structure.
Figure 2. Spectrogram of clean speech (click image to listen)
Figure 3. Spectrogram of packet loss simulated speech (click image to listen)
Why This Matters As online meetings become a permanent part of work and education, unnoticed sound degradation can silently reduce communication quality. By automatically detecting these problems, our approach helps make virtual meetings clearer, fairer, and less tiring—so no one’s voice turns into a “ghost” in the meeting.
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
When driving a hybrid vehicle, many people notice the moment when the quiet electric drive suddenly switches to the engine — and the engine can feel “loud,” even when the actual sound level is modest. Why does this happen? And does the way drivers perceive this noise differ across countries? In this study, we used machine learning to predict how people judge engine noise annoyance and to uncover insights that may help make future hybrid vehicles more comfortable.
Figure 1. AI Model for Predicting Engine Noise Perception
Video 1. On-Road Driving Example for Data Collection
We conducted on-road evaluations in Japan, the United States, and the United Kingdom. During each test, we simultaneously recorded in-cabin sound, vehicle parameters, and drivers’ ratings of engine noise on a three-level scale (“Not noisy,” “Noisy,” “Very noisy”), creating a dataset for AI training. In Japan, we used the series-hybrid Nissan Note e-POWER. In the U.S., where this model is not sold, we reproduced its engine sound on the Nissan Ariya EV, and in the U.K. we used the Qashqai e-POWER engine sound played on the Ariya. Because vehicles, drivers, and road environments differed across regions, the study provided a stringent test of model generality.
Figure 2. On-Road Evaluation Conditions in Japan, the U.S., and the U.K.
First, we tested how accurately AI could predict annoyance using only in-cabin sound data such as loudness and sharpness etc. In Japan, prediction accuracy reached about 57%. When we added three vehicle parameters — engine speed, acceleration torque, and vehicle speed — accuracy increased to 67%, demonstrating that driving conditions, not just sound, play an important role in annoyance perception. The same trend was observed in the U.S. and the U.K.
Figure 3. Prediction Accuracy Improvements Using Vehicle Data and Time History
However, the relative importance of the three vehicle parameters differed by region. In Japan and the U.S., engine speed contributed most strongly to predictions. In contrast, in the U.K., acceleration torque was the most influential factor. This likely reflects the presence of many roundabouts in the U.K. test route, where frequent acceleration and deceleration lead drivers to value the coherence between engine sound and vehicle motion. This aligns with the author’s own experience living in the U.K. for three years.
Next, we incorporated several seconds of engine-speed history into the vehicle parameters. In all regions, adding this short-term history improved prediction accuracy. Although the optimal history length differed slightly — around 5.5 seconds in Japan and 6.5 seconds in the U.S., — the common finding was clear: people judge engine noise not from a single moment but from the pattern of change over several seconds.
Figure 4 Prediction Improvement When Engine-Speed History Is Added
Despite differences in vehicles, traffic environments, and evaluation routes, considering “vehicle operating conditions” together with “recent temporal changes” consistently improved the AI’s ability to predict annoyance across all regions. These findings provide valuable clues for designing hybrid vehicles that feel smoother and more comfortable for drivers around the world.
Instagram: @jason.bickmore
Brigham Young University, Department of Physics and Astronomy, Provo, Utah, 84602, United States
Popular version of 1aCA4 – Feature selection for machine-learned crowd reactions at collegiate basketball games
Presented at the 188th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0037270
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
A mixture of traditional and custom tools is enabling AI to make meaning in an unexplored frontier: crowd noise at sporting events.
The unique link between a crowd’s emotional state and its sound makes crowd noise a promising way to capture feedback about an event continuously and in real-time. Transformed into feedback, crowd noise would help venues improve the experience for fans, sharpen advertisements, and support safety.
To capture this feedback, we turned to machine learning, a popular strategy for making tricky connections. While the tools required to teach AI to interpret speech from a single person are well-understood (think Siri), the tools required to make sense of crowd noise are not.
To find the best tools for this job, we began with a simpler task: teaching an AI model to recognize applause, chanting, distracting the other team, and cheering at college basketball and volleyball games (Fig. 1).
Figure 1: Machine learning identifies crowd behaviors from crowd noise. We helped machine learning models recognize four behaviors: applauding, chanting, cheering, and distracting the other team. Image courtesy of byucougars.com.
We began with a large list of tools, called features, some drawn from traditional speech processing and others created using a custom strategy. After applying five methods to eliminate all but the most powerful features, a blend of traditional and custom features remained. A model trained with these features recognized the four behaviors with at least 70% accuracy.
Based on these results, we concluded that, when interpreting crowd noise, both traditional and custom features have a place. Even though crowd noise is not the situation the traditional tools were designed for, they are still valuable. The custom tools are useful too, complementing the traditional tools and sometimes outperforming them. The tools’ success at recognizing the four behaviors indicates that a similar blend of traditional and custom tools could enable AI models to navigate crowd noise well enough to translate it into real-time feedback. In future work, we will investigate the robustness of these features by checking whether they enable AI to recognize crowd behaviors equally well at events other than college basketball and volleyball games.
Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdańsk, Pomerania, 80-233, Poland
Popular version of 1aSP6 – Strategies for Preprocessing Speech to Enhance Neural Model Efficiency in Speech-to-Text Applications
Presented at the 187th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0034984
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Effective communication in healthcare is essential, as accurate information can directly impact patient care. This paper discusses research aimed at improving speech recognition technology to help medical professionals document patient information more effectively. By using advanced techniques, we can make speech-to-text systems more reliable for healthcare, ensuring they accurately capture spoken information.
In healthcare settings, professionals often need to quickly and accurately record patient interactions. Traditional typing can be slow and error-prone, while speech recognition allows doctors to dictate notes directly into electronic health records (EHRs), saving time and reducing miscommunication.
The main goal of our research was to test various ways of enhancing speech-to-text accuracy in healthcare. We compared several methods to help the system understand spoken language more clearly. These methods included different ways of analyzing sound, like looking at specific sound patterns or filtering background noise.
In this study, we recorded around 80,000 voice samples from medical professionals. These samples were then processed to highlight important speech patterns, making it easier for the system to learn and recognize medical terms. We used a method called Principal Component Analysis (PCA) to keep the data simple while ensuring essential information was retained.
Our findings showed that combining several techniques to capture speech patterns improved system performance. We saw an average accuracy improvement, with fewer word and character recognition errors.
The potential benefits of this work are significant:
Smoother documentation: Medical staff can record notes more efficiently, freeing up time for patient care.
Improved accuracy: Patient records become more reliable, reducing the chance of miscommunication.
Better healthcare outcomes: Enhanced communication can improve the quality of care.
This study highlights the promise of advanced speech recognition in healthcare. With further development, these systems can support medical professionals in delivering better patient care through efficient and accurate documentation.
Figure1. Frontpage of the ADMEDVOICE corpus containing medical text and their spoken equivalents
Queen Mary University of London, Mile End Road, London, England, E1 4NS, United Kingdom
Popular version of 3aSP1-Artificial intelligence in music production: controversy and opportunity, presented at the 183rd ASA Meeting.
Music production
In music production, one typically has many sources. They each need to be heard simultaneously, but can all be created in different ways, in different environments and with different attributes. The mix should have all sources sound distinct yet contribute to a nice clean blend of the sounds. To achieve this is labour intensive and requires a professional engineer. Modern production systems help, but they’re incredibly complex and all require manual manipulation. As technology has grown, it has become more functional but not simpler for the user.
Intelligent music production
Intelligent systems could analyse all the incoming signals and determine how they should be modified and combined. This has the potential to revolutionise music production, in effect putting a robot sound engineer inside every recording device, mixing console or audio workstation. Could this be achieved? This question gets to the heart of what is art and what is science, what is the role of the music producer and why we prefer one mix over another.
Figure 1 Caption: The architecture of an automatic mixing system. [Image courtesy of the author]
Perception of mixing
But there is little understanding of how we perceive audio mixes. Almost all studies have been restricted to lab conditions; like measuring the perceived level of a tone in the presence of background noise. This tells us very little about real world cases. It doesn’t say how well one can hear lead vocals when there are guitar, bass and drums.
Best practices
And we don’t know why one production will sound dull while another makes you laugh and cry, even though both are on the same piece of music, performed by competent sound engineers. So we needed to establish what is good production, how to translate it into rules and exploit it within algorithms. We needed to step back and explore more fundamental questions, filling gaps in our understanding of production and perception.
Knowledge engineering
We used an approach that incorporated one of the earliest machine learning methods, knowledge engineering. Its so old school that its gone out of fashion. It assumes experts have already figured things out, they are experts after all. So let’s capture best practices as a set of rules and processes. But this is no easy task. Most sound engineers don’t know what they did. Ask a famous producer what he or she did on a hit song and you often get an answer like ‘I turned the knob up to 11 to make it sound phat.” How do you turn that into a mathematical equation? Or worse, they say it was magic and can’t be put into words.
We systematically tested all the assumptions about best practices and supplemented them with listening tests that helped us understand how people perceive complex sound mixtures. We also curated multitrack audio, with detailed information about how it was recorded, multiple mixes and evaluations of those mixes.
This enabled us to develop intelligent systems that automate much of the music production process.
Video Caption: An automatic mixing system based on a technology we developed.
Transformational impact
I gave a talk about this once in a room that had panel windows all around. These talks are usually half full. But this time it was packed, and I could see faces outside pressed up against the windows. They all wanted to find out about this idea of automatic mixing. It’s a unique opportunity for academic research to have transformational impact on an entire industry. It addresses the fact that music production technologies are often not fit for purpose. Intelligent systems open up new opportunities. Amateur musicians can create high quality mixes of their content, small venues can put on live events without needing a professional engineer, time and preparation for soundchecks could be drastically reduced, and large venues and broadcasters could significantly cut manpower costs.
Taking away creativity
Its controversial. We entered an automatic mix in a student recording competition as a sort of Turing Test. Technically we cheated, because the mixes were supposed to be made by students, not by an ‘artificial intelligence’ (AI) created by a student. Afterwards I asked the judges what they thought of the mix. The first two were surprised and curious when I told them how it was done. The third judge offered useful comments when he thought it was a student mix. But when I told him that it was an ‘automatic mix’, he suddenly switched and said it was rubbish and he could tell all along.
Mixing is a creative process where stylistic decisions are made. Is this taking away creativity, is it taking away jobs? Such questions come up time and time again with new technologies, going back to 19th century protests by the Luddites, textile workers who feared that time spent on their skills and craft would be wasted as machines could replace their role in industry.
Not about replacing sound engineers
These are valid concerns, but its important to see other perspectives. A tremendous amount of music production work is technical, and audio quality would be improved by addressing these problems. As the graffiti artist Banksy said “All artists are willing to suffer for their work. But why are so few prepared to learn to draw?”
Creativity still requires technical skills. To achieve something wonderful when mixing music, you first have to achieve something pretty good and address issues with masking, microphone placement, level balancing and so on.
Video Caption: Time offset (comb filtering) correction, a technical problem in music production solved by an intelligent system.
The real benefit is not replacing sound engineers. Its dealing with all those situations when a talented engineer is not available; the band practicing in the garage, the small restaurant venue that does not provide any support, or game audio, where dozens of sounds need to be mixed and there is no miniature sound engineer living inside the games console.
Figure 2. On-Road Evaluation Conditions in Japan, the U.S., and the U.K.
Figure 3. Prediction Accuracy Improvements Using Vehicle Data and Time History
Figure 4 Prediction Improvement When Engine-Speed History Is Added
Figure 1: Machine learning identifies crowd behaviors from crowd noise. We helped machine learning models recognize four behaviors: applauding, chanting, cheering, and distracting the other team. Image courtesy of byucougars.com.
Figure1. Frontpage of the ADMEDVOICE corpus containing medical text and their spoken equivalents![Artificial Intelligence Figure 1: The architecture of an automatic mixing system. [Image courtesy of the author]](https://i0.wp.com/acoustics.org/wp-content/uploads/2022/11/Picture1-1.png?resize=1080%2C1086&ssl=1)
Figure 1 Caption: The architecture of an automatic mixing system. [Image courtesy of the author]
