Respiratory Rate Estimation from Audio Using Object Detection with Learnable Spectrograms

Sound event detection models commonly rely on spectrogram representations of audio signals and recent approaches have adapted image-based object detection architectures to acoustic domains. This paradigm is suitable for respiratory monitoring, where breathing events are visually distinguishable even under noisy conditions. In this study, we propose a Representation Enhancement for Neural Imaging (RENI) framework that combines a modified You Only Look Once (YOLO) object detection head with a trainable spectrogram front-end implemented using nnAudio. The front-end enables GPU-accelerated waveform-to-spectrogram conversion while allowing adaptive learning of Short-Time Fourier Transform (STFT) and Melody (Mel) basis functions. The model was trained for breathing-phase localization and respiratory rate estimation from 44.1 kHz audio recordings acquired during exercise. The results show that the trainable Mel representation improves respiratory-rate accuracy compared with static and trainable STFT configurations, achieving a mean absolute error of 1.15 breaths per minute. Bootstrap 95% confidence intervals and one-sided permutation tests show statistically significant gains for selected trainable STFT and Mel configurations under the min-MAE confidence thresholding protocol, while pooled effects remain directionally favorable for the trainable Mel front-end. These findings demonstrate improved exhale-based respiratory rate estimation under the studied conditions, while broader external validation is still required.