SearcharxivSearch

arXiv · 2403.13643

Vibration Sensitivity of one-port and two-port MEMS microphones

Abstract

Micro-electro-mechanical system (MEMS) microphones (mics) with two acoustic ports are currently receiving considerable interest, with the promise of achieving higher directional sensitivity compared to traditional one-port architectures. However, measuring pressure differences in two-port microphones typically commands sensing elements that are softer than in one-port mics, and are therefore presumably more prone to interference from external vibration. Here we derive a universal expression for microphone sensitivity to vibration and we experimentally demonstrate its validity for several emerging two-port microphone technologies. We also perform vibration measurements on a one-port mic, thus providing a one-stop direct comparison between one-port and two-port sensing approaches. We find that the acoustically-referred vibration sensitivity of two-port MEMS mics, in units of measured acoustic pressure per external acceleration (i.e., Pascals per g), does not depend on the sensing element stiffness nor on its natural frequency. We also show that this vibration sensitivity in two-port mics is inversely proportional to frequency as opposed to the frequency independent behavior observed in one-port mics. This is confirmed experimentally for several types of microphone packages.

Explore related subjects

Keep this discovery

BibTeXRIS

Francis Doyon-D'Amour, Carly Stalder, Timothy Hodges, Michel Stephan, Lixiue Wu, Triantafillos Koukoulas, Stephane Leahy, Raphael St-Gelais. 2024-03-20. Vibration Sensitivity of one-port and two-port MEMS microphones. https://arxiv.org/abs/2403.13643

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Diarization Error Decomposition Under Pause Annotation Ambiguity

Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morphological closing, which has been used for pause-tolerant diarization evaluation, discards segment-level distinctions. Instead, we propose an exact, overlap-aware decomposition of standard DER into a pause-attributable component, consisting of errors compatible with pause filling, and a residual core component that can serve as a proxy for intrinsic diarization errors. The decomposition leaves DER unchanged, while the pause-attributable and core components vary monotonically with the pause threshold and eventually saturate. Experiments spanning synthetic transformations, annotation mismatch, cross-domain evaluation, and tight-boundary diarization show that the decomposition reveals error sources not apparent from standard DER.

eess.AS

Less can be More: What Aspects of Speech Drive End-of-Turn Detection

In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.

eess.AS

Downstream-Task-Aware Unified Source Separation

Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account for downstream task requirements, such as whether the enhanced speech will be used for human listening or automatic speech recognition (ASR). In this paper, we propose a prompt extension framework for TUSS that incorporates downstream task information into the input prompts and switches the loss function according to the given prompt during training, enabling outputs with different signal characteristics at inference time. Specifically, we introduce an ASR-dedicated prompt paired with a regularized loss function that reduces speech artifacts to improve ASR robustness, while the standard prompt is paired with the conventional SNR loss function. Experiments on the LibriSpeech and JNAS corpora demonstrate that the proposed joint-training scheme enables a single model to improve ASR performance over noisy input across a wide range of SNR conditions by selecting the ASR-dedicated prompt, while maintaining general speech enhancement quality when the standard prompt is used.

eess.AS