arXiv - CS - Sound最新文献

Benchmarking Sub-Genre Classification For Mainstage Dance Music 主舞台舞曲子流派分类基准测试

arXiv - CS - Sound Pub Date : 2024-09-10 DOI: arxiv-2409.06690

Hongzhi Shu, Xinglin Li, Hongyu Jiang, Minghao Fu, Xinyu Li

引用次数: 0

Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks 利用从机器特定滤波器库中提取的频谱-时态调制表示法进行机器异常声音检测

arXiv - CS - Sound Pub Date : 2024-09-09 DOI: arxiv-2409.05319

Kai Li, Khalid Zaman, Xingfeng Li, Masato Akagi, Masashi Unoki

{"title":"Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks","authors":"Kai Li, Khalid Zaman, Xingfeng Li, Masato Akagi, Masashi Unoki","doi":"arxiv-2409.05319","DOIUrl":"https://doi.org/arxiv-2409.05319","url":null,"abstract":"Early detection of factory machinery malfunctions is crucial in industrial\u0000applications. In machine anomalous sound detection (ASD), different machines\u0000exhibit unique vibration-frequency ranges based on their physical properties.\u0000Meanwhile, the human auditory system is adept at tracking both temporal and\u0000spectral dynamics of machine sounds. Consequently, integrating the\u0000computational auditory models of the human auditory system with\u0000machine-specific properties can be an effective approach to machine ASD. We\u0000first quantified the frequency importances of four types of machines using the\u0000Fisher ratio (F-ratio). The quantified frequency importances were then used to\u0000design machine-specific non-uniform filterbanks (NUFBs), which extract the log\u0000non-uniform spectrum (LNS) feature. The designed NUFBs have a narrower\u0000bandwidth and higher filter distribution density in frequency regions with\u0000relatively high F-ratios. Finally, spectral and temporal modulation\u0000representations derived from the LNS feature were proposed. These proposed LNS\u0000feature and modulation representations are input into an autoencoder\u0000neural-network-based detector for ASD. The quantification results from the\u0000training set of the Malfunctioning Industrial Machine Investigation and\u0000Inspection dataset with a signal-to-noise (SNR) of 6 dB reveal that the\u0000distinguishing information between normal and anomalous sounds of different\u0000machines is encoded non-uniformly in the frequency domain. By highlighting\u0000these important frequency regions using NUFBs, the LNS feature can\u0000significantly enhance performance using the metric of AUC (area under the\u0000receiver operating characteristic curve) under various SNR conditions.\u0000Furthermore, modulation representations can further improve performance.\u0000Specifically, temporal modulation is effective for fans, pumps, and sliders,\u0000while spectral modulation is particularly effective for valves.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harmonic Reasoning in Large Language Models 大型语言模型中的谐波推理

arXiv - CS - Sound Pub Date : 2024-09-09 DOI: arxiv-2409.05521

Anna Kruspe

引用次数: 0

Evaluation of real-time transcriptions using end-to-end ASR models 使用端到端 ASR 模型对实时转录进行评估

arXiv - CS - Sound Pub Date : 2024-09-09 DOI: arxiv-2409.05674

Carlos Arriaga, Alejandro Pozo, Javier Conde, Alvaro Alonso

{"title":"Evaluation of real-time transcriptions using end-to-end ASR models","authors":"Carlos Arriaga, Alejandro Pozo, Javier Conde, Alvaro Alonso","doi":"arxiv-2409.05674","DOIUrl":"https://doi.org/arxiv-2409.05674","url":null,"abstract":"Automatic Speech Recognition (ASR) or Speech-to-text (STT) has greatly\u0000evolved in the last few years. Traditional architectures based on pipelines\u0000have been replaced by joint end-to-end (E2E) architectures that simplify and\u0000streamline the model training process. In addition, new AI training methods,\u0000such as weak-supervised learning have reduced the need for high-quality audio\u0000datasets for model training. However, despite all these advancements, little to\u0000no research has been done on real-time transcription. In real-time scenarios,\u0000the audio is not pre-recorded, and the input audio must be fragmented to be\u0000processed by the ASR systems. To achieve real-time requirements, these\u0000fragments must be as short as possible to reduce latency. However, audio cannot\u0000be split at any point as dividing an utterance into two separate fragments will\u0000generate an incorrect transcription. Also, shorter fragments provide less\u0000context for the ASR model. For this reason, it is necessary to design and test\u0000different splitting algorithms to optimize the quality and delay of the\u0000resulting transcription. In this paper, three audio splitting algorithms are\u0000evaluated with different ASR models to determine their impact on both the\u0000quality of the transcription and the end-to-end delay. The algorithms are\u0000fragmentation at fixed intervals, voice activity detection (VAD), and\u0000fragmentation with feedback. The results are compared to the performance of the\u0000same model, without audio fragmentation, to determine the effects of this\u0000division. The results show that VAD fragmentation provides the best quality\u0000with the highest delay, whereas fragmentation at fixed intervals provides the\u0000lowest quality and the lowest delay. The newly proposed feedback algorithm\u0000exchanges a 2-4% increase in WER for a reduction of 1.5-2s delay, respectively,\u0000to the VAD splitting.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification PDAF：用于验证说话人的语音去重注意框架

arXiv - CS - Sound Pub Date : 2024-09-09 DOI: arxiv-2409.05799

Massa Baali, Abdulhamid Aldoobi, Hira Dhamyal, Rita Singh, Bhiksha Raj

引用次数: 0

Evaluating Neural Networks Architectures for Spring Reverb Modelling 评估用于弹簧混响建模的神经网络架构

arXiv - CS - Sound Pub Date : 2024-09-08 DOI: arxiv-2409.04953

Francesco Papaleo, Xavier Lizarraga-Seijas, Frederic Font

引用次数: 0

From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems 从计算到消耗：探索用于 SED 系统的神经网络训练和测试的计算-能源联系

arXiv - CS - Sound Pub Date : 2024-09-08 DOI: arxiv-2409.05080

Constance Douwes, Romain Serizel

引用次数: 0

Clustering of Indonesian and Western Gamelan Orchestras through Machine Learning of Performance Parameters 通过对演奏参数的机器学习对印度尼西亚和西方加麦兰管弦乐队进行分类

arXiv - CS - Sound Pub Date : 2024-09-03 DOI: arxiv-2409.03713

Simon Linke, Gerrit Wendt, Rolf Bader

引用次数: 0

Sample-Efficient Diffusion for Text-To-Speech Synthesis 文本到语音合成的样本高效扩散

arXiv - CS - Sound Pub Date : 2024-09-01 DOI: arxiv-2409.03717

Justin Lovelace, Soham Ray, Kwangyoun Kim, Kilian Q. Weinberger, Felix Wu

引用次数: 0

Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation 光学音乐识别中的知识发现：利用实例分割加强信息检索

arXiv - CS - Sound Pub Date : 2024-08-27 DOI: arxiv-2408.15002

Elona Shatri, George Fazekas

{"title":"Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation","authors":"Elona Shatri, George Fazekas","doi":"arxiv-2408.15002","DOIUrl":"https://doi.org/arxiv-2408.15002","url":null,"abstract":"Optical Music Recognition (OMR) automates the transcription of musical\u0000notation from images into machine-readable formats like MusicXML, MEI, or MIDI,\u0000significantly reducing the costs and time of manual transcription. This study\u0000explores knowledge discovery in OMR by applying instance segmentation using\u0000Mask R-CNN to enhance the detection and delineation of musical symbols in sheet\u0000music. Unlike Optical Character Recognition (OCR), OMR must handle the\u0000intricate semantics of Common Western Music Notation (CWMN), where symbol\u0000meanings depend on shape, position, and context. Our approach leverages\u0000instance segmentation to manage the density and overlap of musical symbols,\u0000facilitating more precise information retrieval from music scores. Evaluations\u0000on the DoReMi and MUSCIMA++ datasets demonstrate substantial improvements, with\u0000our method achieving a mean Average Precision (mAP) of up to 59.70% in dense\u0000symbol environments, achieving comparable results to object detection.\u0000Furthermore, using traditional computer vision techniques, we add a parallel\u0000step for staff detection to infer the pitch for the recognised symbols. This\u0000study emphasises the role of pixel-wise segmentation in advancing accurate\u0000music symbol recognition, contributing to knowledge discovery in OMR. Our\u0000findings indicate that instance segmentation provides more precise\u0000representations of musical symbols, particularly in densely populated scores,\u0000advancing OMR technology. We make our implementation, pre-processing scripts,\u0000trained models, and evaluation results publicly available to support further\u0000research and development.","PeriodicalId":501178,"journal":{"name":"arXiv - CS - Sound","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0