噪声环境下基于能量检测的生态声音识别

无效声音段的存在导致噪声环境下声音识别方法的识别率迅速降低。为此,提出一种基于能量检测的抗噪声音识别方法。用能量检测方法检测背景噪声环境下的有用声音信号,对检测到的信号提取Mel频率倒谱系数特征,使用支持向量机对提取的特征向量训练分类模型,对含有噪声的生态环境声音进行识别。实验结果表明,该方法具有较好的抗噪能力,其在信噪比40 dB以下的识别率比添加能量检测前提高约25%。     

基于PDA平台的实时说话人识别系统

受到模型规模大和计算量大的限制,经典的基于高斯混合模型的说话人识别方法不适合于资源有限的PDA平台实时说话人自动识别要求。以Mel倒谱系数为说话人特征,运用主成分分类技术,结合定点数计算技术实现实时说话人自动识别。在19个用户的语音库上进行系统识别实验,此新型分类技术的训练时间缩短为基线系统的1/50,测试时间缩短为1/12,模型规模缩小为1/6,同时识别性能达到94.7%。     

基于稀疏非负矩阵分解的低空声目标识别

借鉴人耳听觉原理和特征学习的优势,提出了梅尔(Mel)频率谱提取和稀疏非负矩阵分解相结合的方法用于低空飞行目标声信号识别。首先,以不同目标的Mel频率谱为特征矩阵,利用稀疏非负矩阵分解方法学习得到各自的模板矩阵;然后,利用按列合并后的模板矩阵对训练/测试样本进行特征分解获得编码系数,该系数可作为分类特征;最后,结合不同目标的特点,采用分频段特征提取和顺序二类分类的方法进行多目标分类,并与Mel频率倒谱系数进行性能比较。结果显示,无论在单类目标辨识还是在多类目标分类中,稀疏非负矩阵分解方法均取得了更好的效果。     

基于TMS320C6713的语音识别系统设计

通过选用德州仪器公司带浮点功能的TMS320C6713DSP芯片作为系统核心处理器,结合MSP430单片机作为外围控制器,给出了一种实时语音识别系统的设计方法。该系统核心算法采用美尔频率倒谱系数作为特征参数进行特征提取和动态时间规整(DTW)算法进行模式匹配。通过编程调试,该系统具有良好的灵活性和实时性,在抗噪声、鲁棒性和识别率等方面有明显的提高。该系统在许多领域可作为实用化的一种参考。     

Automatic speaker recognition from speech signal using bidirectional long-short-term memory recurrent neural network

Speaker recognition is a major challenge in various languages for researchers. For programmed speaker recognition structure prepared by utilizing ordinary speech, shouting creates a confusion between the enlistment and test, henceforth minimizing the identification execution as extreme vocal exertion is required during shouting. Speaker recognition requires more time for classification of data, accuracy is optimized, and the low root-mean-square error rate is the major problem. The objective of this work is to develop an efficient system of speaker recognition. In this work, an improved method of Wiener filter algorithm is applied for better noise reduction. To obtain the essential feature vector values, Mel-frequency cepstral coefficient feature extraction method is used on the noise-removed signals. Furthermore, input samples are created by using these extracted features after the dimensions have been reduced using probabilistic principal component analysis. Finally, recurrent neural network-bidirectional long-short-term memory is used for the classification to improve the prediction accuracy. For checking the effectiveness, the proposed work is compared with the existing methods based on accuracy, sensitivity, and error rate. The results obtained with the proposed method demonstrate an accuracy of 95.77%.