Noise pollution tends to receive less awareness compared to other types of pollution,however,it greatly impacts the quality of life for humans such as causing sleep disruption,stress or hearing impairment.Profiling ur...Noise pollution tends to receive less awareness compared to other types of pollution,however,it greatly impacts the quality of life for humans such as causing sleep disruption,stress or hearing impairment.Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control,planning of the soundscape environment,or selection of safe living space.In this paper,we proposed a self-attention long short-term memory(LSTM)method that can improve sound classification compared to previous baselines.An attention mechanism will be designed solely to capture the key section of an audio data series.This is practical as we only need to process important parts of the data and can ignore the rest,making it applicable when gathering information with long-term dependencies.The dataset used is the Urbansound8k dataset which specifically pertains to urban environments and data augmentation was applied to overcome imbalanced data and dataset scarcity.All audio sources in the dataset were normalized to mono signals.From the dataset above,an experiment was conducted to confirm the suitability of the proposed model when applied to the mel-spectrogram and MFCC(Mel-Frequency Cepstral Coefficients)datasets transformed from the original dataset.Improving the classification accuracy depends on the machine learning models as well as the input data,therefore we have evaluated different class models and extraction methods to find the best performing.By combining data augmentation techniques and various extraction methods,our classification model has achieved state-of-the-art performance,each class accuracy is up to 98%.展开更多
枪声识别技术在军事环境下可以快速准确地提供战场信息,但是目前大部分枪声识别系统均部署在服务器端,实用性和可行性不高,针对这一问题,本文设计了一种基于ZYNQ的枪声识别系统。该系统以ZYNQ7020芯片为核心,充分利用ZYNQ芯片集ARM与FPG...枪声识别技术在军事环境下可以快速准确地提供战场信息,但是目前大部分枪声识别系统均部署在服务器端,实用性和可行性不高,针对这一问题,本文设计了一种基于ZYNQ的枪声识别系统。该系统以ZYNQ7020芯片为核心,充分利用ZYNQ芯片集ARM与FPGA于一体的特性,首先在芯片的FPGA部分设计了多通道数据传输链路和声场特征参数提取模块;其次在芯片的ARM部分部署经过PC端训练后的轻量化网络模型,对经过FPGA提取的特征参数进行处理,进而实现对枪声种类的识别;最后使用枪声数据集NIJ Grant 2016-DN-BX-0183中的3种枪声在外场进行试验。试验结果表明,该系统能够准确地对枪声进行分类,枪声的平均识别率达到91.67%。该成果在枪声识别领域具有较强的应用价值。展开更多
文摘Noise pollution tends to receive less awareness compared to other types of pollution,however,it greatly impacts the quality of life for humans such as causing sleep disruption,stress or hearing impairment.Profiling urban sound through the identification of noise sources in cities could help to benefit livability by reducing exposure to noise pollution through methods such as noise control,planning of the soundscape environment,or selection of safe living space.In this paper,we proposed a self-attention long short-term memory(LSTM)method that can improve sound classification compared to previous baselines.An attention mechanism will be designed solely to capture the key section of an audio data series.This is practical as we only need to process important parts of the data and can ignore the rest,making it applicable when gathering information with long-term dependencies.The dataset used is the Urbansound8k dataset which specifically pertains to urban environments and data augmentation was applied to overcome imbalanced data and dataset scarcity.All audio sources in the dataset were normalized to mono signals.From the dataset above,an experiment was conducted to confirm the suitability of the proposed model when applied to the mel-spectrogram and MFCC(Mel-Frequency Cepstral Coefficients)datasets transformed from the original dataset.Improving the classification accuracy depends on the machine learning models as well as the input data,therefore we have evaluated different class models and extraction methods to find the best performing.By combining data augmentation techniques and various extraction methods,our classification model has achieved state-of-the-art performance,each class accuracy is up to 98%.
文摘枪声识别技术在军事环境下可以快速准确地提供战场信息,但是目前大部分枪声识别系统均部署在服务器端,实用性和可行性不高,针对这一问题,本文设计了一种基于ZYNQ的枪声识别系统。该系统以ZYNQ7020芯片为核心,充分利用ZYNQ芯片集ARM与FPGA于一体的特性,首先在芯片的FPGA部分设计了多通道数据传输链路和声场特征参数提取模块;其次在芯片的ARM部分部署经过PC端训练后的轻量化网络模型,对经过FPGA提取的特征参数进行处理,进而实现对枪声种类的识别;最后使用枪声数据集NIJ Grant 2016-DN-BX-0183中的3种枪声在外场进行试验。试验结果表明,该系统能够准确地对枪声进行分类,枪声的平均识别率达到91.67%。该成果在枪声识别领域具有较强的应用价值。
