Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model

Chiranjibi Sitaula; Jinyuan He; Archana Priyadarshi; Mark Tracy; Omid Kavehei; Murray Hinder; Anusha Withana; Alistair McEwan; Faezeh Marzbanrad

doi:10.1109/taslp.2022.3178225

arXiv · 2108.07467

Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model

Chiranjibi Sitaula·Jinyuan He·Archana Priyadarshi·Mark Tracy·Omid Kavehei·Murray Hinder·Anusha Withana·Alistair McEwan·Faezeh Marzbanrad

View original source ↗Read source document ↗Export metadata ↓

Abstract

Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care. It helps early detection of neonatal bowel dysfunctions and allows timely intervention. This paper presents a neonatal bowel sound detection method to assist the auscultation. Specifically, a Convolutional Neural Network (CNN) is proposed to classify peristalsis and non-peristalsis sounds. The classification is then optimized using a Laplace Hidden Semi-Markov Model (HSMM). The proposed method is validated on abdominal sounds from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results show that the method can effectively detect bowel sounds with accuracy and area under curve (AUC) score being 89.81% and 83.96% respectively, outperforming 13 baseline methods. Furthermore, the proposed Laplace HSMM refinement strategy is proven capable to enhance other bowel sound detection models. The outcomes of this work have the potential to facilitate future telehealth applications for neonatal care. The source code of our work can be found at: https://bitbucket.org/chirudeakin/neonatal-bowel-sound-classification/

Explore related subjects

cs.SD cs.LG eess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Chiranjibi Sitaula, Jinyuan He, Archana Priyadarshi, Mark Tracy, Omid Kavehei, Murray Hinder, Anusha Withana, Alistair McEwan, Faezeh Marzbanrad. 2022-06-01. Neonatal Bowel Sound Detection Using Convolutional Neural Network and Laplace Hidden Semi-Markov Model. https://doi.org/10.1109/taslp.2022.3178225

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

KEEP EXPLORING

Related papers

arXiv2026

ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis

Persian remains substantially underrepresented in open speech-text resources, limiting progress in multi-speaker text-to-speech (TTS), speech-language modelling, and low-resource speech processing. We introduce ParsVoice, the largest publicly available Persian speech-text corpus tailored for training multi-speaker TTS systems, along with a scalable pipeline to construct high-quality speech-text data from long-form audiobook recordings. The pipeline combines a fine-tuned ParsBERT sentence-completion classifier, ASR-based boundary optimization, punctuation restoration, speaker identification, and a multi-dimensional quality assessment that covers both audio and Persian-specific text properties. The resulting release contains a 2,200-hour TTS-ready subset with 1.36 million aligned segments from 1,815 automatically inferred speaker IDs, making it more than 25 times larger than the largest previously available open Persian TTS dataset. To validate the corpus, we fine-tune XTTSv2, a zero-shot multilingual TTS model that operates directly on raw Persian text without phoneme representations. The resulting model achieves a naturalness MOS of 3.6/5 and a speaker-similarity MOS of 4.0/5. ParsVoice, its metadata, and the corpus-construction pipeline are publicly available on Hugging Face: https://huggingface.co/datasets/MohammadJRanjbar/ParsVoice and GitHub: https://github.com/MohammadJRanjbar/ParsVoice, supporting reproducible research on Persian speech synthesis and low-resource speech-language technologies.

cs.SD↗

arXiv2026

Speech Generation Speaker Poisoning: Capability Erasure in Zero-Shot Text-to-Speech

Recent zero-shot Text-to-Speech (TTS) systems can clone previously unseen voices from only a few seconds of audio. We formulate Speech Generation Speaker Poisoning (SGSP), a task that seeks to prevent a model from synthesizing targeted speaker identities while maintaining performance on all other speakers. Unlike conventional machine unlearning, removing training examples is insufficient because modern zero-shot TTS systems can reconstruct identities through learned speaker representations and strong generalization capabilities. We evaluate both inference-time filtering and parameter-modification approaches across settings involving 1, 15, and 100 forget speakers, focusing primarily on speakers seen during training, which we show are harder to suppress than unseen speakers. To characterize the trade-off between utility and privacy, we introduce an evaluation framework based on AUC analysis and a proposed metric, Forget Set Similarity (FSSIM). Our results demonstrate effective speaker suppression for up to 15 forget speakers while revealing that worst-case identity leakage (Max-FSSIM) remains unresolved at multi-speaker scale - establishing this as an open challenge for future work. Together, our work establishes targeted speaker poisoning as a task for zero-shot TTS and, using StyleTTS2 as an initial testbed, provides methods and evaluation protocols for future research, with code and model weights.

cs.SD↗

arXiv2026

Tracing the Origins: Legacy Codec Identification in Neural Audio Transcoding

Residual Vector Quantization (RVQ)-based neural audio codecs (NACs) enable high-fidelity audio distribution at unprecedentedly low bitrates through discrete token-based representations. However, this shift disrupts traditional forensics, as non-linear neural transcoding obscures the underlying traces of legacy compression. This study defines the forensic gap and proposes a Transformer-based framework designed to leverage the hierarchical and temporal dependencies inherent in RVQ sequences. By modeling inter-layer causal relationships and dynamic forensic significance, our model effectively disentangles superimposed artifacts from legacy-to-neural transcoding. Experimental results achieve 97%+ accuracy for codec identification and robust joint identification performance across 32-128 kbps. These results demonstrate that traditional codec traces persist even after neural transcoding, supporting the feasibility and necessity of neural-codec-aware audio forensics.

cs.SD↗