How to find the best material for making exciter-based plate speakers

David Anderson – and10445@d.umn.edu

Instagram: @earthtoneselectronics
Assistant Professor- Electrical Engineering, University of Minnesota Duluth, Duluth, Minnesota, 55812, United States

Popular version of 2aEA1 – A Method for Comparing Candidate Materials in Subjective Tests of Flat-Panel Loudspeakers
Presented at the 187th ASA Meeting
Read the abstract at https://eppro01.ativ.me//web/index.php?page=Session&project=ASAFALL24&id=3771459

–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–


Exciters are devices that can be stuck to just about anything in order to make a speaker. Many DIY speaker makers wonder what material is going to sound the best for their product or home speaker project. The sound of an exciter-based plate speaker depends on many things. These include the plate materials, size, shape, and where the exciter is attached. The fact that there are so many factors that control the sound makes it difficult to directly compare the sound of materials. For example, a plastic and aluminum plate of the same size and thickness will have completely different frequency ranges when set up as speakers. In this paper, a method is proposed to calculate the required shape and size of any set of materials so that speakers made from them will have the same loudness and frequency range and the effect of the materials on the speaker sound can be easily compared.

Equations derived in the paper demonstrate that the vibrations and volumes of plates made from different materials will match when they have the same length-to-width ratio and weight (volume times density). Three different materials (Foam poster board, plastic, and aluminum) were chosen for comparison in this paper because they are commonly used by DIY makers to create speakers. Figure 1 shows the simulated relative loudness over a range of audio frequencies for three different materials (foam poster board, plastic, and aluminum) with the same length-to-width ratio and weight. The loudness graphs mostly overlap, but the volumes diverge at high frequencies because the ring shape of the exciter interacts differently with each material. This effect can be mitigated by using a smaller exciter.

Figure 1 – Simulated speaker loudness using three different materials with matching length-to-width ratios and weights.

Simulated plate responses are then compared with experimentally measured loudness results using actual plates made from plastic, aluminum, and foam poster board. These comparisons shown in Figure 2 allow us to identify whether there are any material-specific deviations from the simulated response that would lend each material its unique “sound.”

Figure 2 – Simulated vs. experimentally measured plate loudness for three different materials.

The plastic and aluminum plates match their simulations closely. The aluminum plate has sharper peaks than the plastic plate, indicating a more “hollow” sound. The foam poster board does not match its simulation well, showing that this material adds a distinctive “color” to the sound at mid-range and high audio frequencies.

Applying this method to additional materials that DIY speaker builders use like wood, cardboard, and foam insulation can shed light on their unique “sounds” as well.

Enhancing Speech Recognition in Healthcare

Andrzej Czyzewski – andczyz@gmail.com

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://eppro01.ativ.me//web/index.php?page=IntHtml&project=ASAFALL24&id=3771522

–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