基于梅尔频谱的音频信号分类识别(Pytorch)

刺骨的言语ヽ痛彻心扉 2022-09-14 09:41 245阅读 0赞

本项目将使用Pytorch，实现一个简单的的音频信号分类器，可应用于机械信号分类识别，鸟叫声信号识别等应用场景。

项目使用librosa进行音频信号处理，backbone使用mobilenet\_v2，在Urbansound8K数据上，最终收敛的准确率在训练集99%，测试集82%，如果想进一步提高识别准确率可以使用更重的backbone和更多的数据增强方法。

完整的项目代码：[https://download.csdn.net/download/guyuealian/30306697][https_download.csdn.net_download_guyuealian_30306697]

尊重原创，转载请注明出处：[https://panjinquan.blog.csdn.net/article/details/120601437][https_panjinquan.blog.csdn.net_article_details_120601437]

--------------------

**目录**

1. 项目结构

2. 环境配置

3.数据处理

（1）数据集Urbansound8K

（2）自定义数据集

（3）音频特征提取:

4.训练Pipeline

5.预测demo.py

--------------------

## 1. 项目结构 ##

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_10_color_FFFFFF_t_70_g_se_x_16][]

--------------------

## 2. 环境配置 ##

使用pip命令安装libsora和pyaudio,pydub等库

pip install librosa
    pip install pyaudio
    pip install pydub

--------------------

## 3.数据处理 ##

###    （1）数据集Urbansound8K  ###

Urbansound8K是目前应用较为广泛的用于自动城市环境声分类研究的公共数据集，  
包含10个分类：**空调声、汽车鸣笛声、儿童玩耍声、狗叫声、钻孔声、引擎空转声、枪声、手提钻、警笛声和街道音乐声**。

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_13_color_FFFFFF_t_70_g_se_x_16][]  
**数据集下载**：[https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz][https_zenodo.org_record_1203745_files_UrbanSound8K.tar.gz]

### （2）自定义数据集 ###

可以自己录制音频信号，制作自己的数据集，参考\[audio/dataloader/record\_audio.py\]  
每个文件夹存放一个类别的音频数据，每条音频数据长度在3秒左右,建议每类的音频数据均衡  
生产train和test数据列表：参考\[audio/dataloader/create\_data.py\]

### （3）音频特征提取:  ###

音频信号是一维的语音信号，不能直接用于模型训练，需要使用librosa将音频转为**梅尔频谱（Mel Spectrogram）**。

librosa提供python接口，在音频、乐音信号的分析中经常用到

wav, sr = librosa.load(data_path, sr=16000)
    # 使用librosa获得音频的梅尔频谱
    spec_image = librosa.feature.melspectrogram(y=wav, sr=sr, hop_length=256)

关于librosa的使用方法，请参考：

1.   [音频特征提取——librosa工具包使用 ][librosa]
2.   [梅尔频谱(mel spectrogram)原理与使用 ][mel spectrogram]

## 4.训练Pipeline ##

（1）构建训练和测试数据

def build_dataset(self, cfg):
            """构建训练数据和测试数据"""
            input_shape = eval(cfg.input_shape)
            # 获取数据
            train_dataset = AudioDataset(cfg.train_data, data_dir=cfg.data_dir, mode='train', spec_len=input_shape[3])
            train_loader = DataLoader(dataset=train_dataset, batch_size=cfg.batch_size, shuffle=True,
                                      num_workers=cfg.num_workers)
    
            test_dataset = AudioDataset(cfg.test_data, data_dir=cfg.data_dir, mode='test', spec_len=input_shape[3])
            test_loader = DataLoader(dataset=test_dataset, batch_size=cfg.batch_size, shuffle=False,
                                     num_workers=cfg.num_workers)
            print("train nums:{}".format(len(train_dataset)))
            print("test  nums:{}".format(len(test_dataset)))
            return train_loader, test_loader

由于librosa.load加载音频数据特别慢，建议使用cache先进行缓存，方便加速

def load_audio(audio_file, cache=False):
        """
        加载并预处理音频
        :param audio_file:
        :param cache: librosa.load加载音频数据特别慢，建议使用进行缓存进行加速
        :return:
        """
        # 读取音频数据
        cache_path = audio_file + ".pk"
        # t = librosa.get_duration(filename=audio_file)
        if cache and os.path.exists(cache_path):
            tmp = open(cache_path, 'rb')
            wav, sr = pickle.load(tmp)
        else:
            wav, sr = librosa.load(audio_file, sr=16000)
            if cache:
                f = open(cache_path, 'wb')
                pickle.dump([wav, sr], f)
                f.close()
        # Compute a mel-scaled spectrogram: 梅尔频谱图
        spec_image = librosa.feature.melspectrogram(y=wav, sr=sr, hop_length=256)
        return spec_image

（2）构建backbone模型

backbone是一个基于CNN+FC的网络结构，与图像CNN分类模型不同的是，图像CNN分类模型的输入维度(batch,3,H,W)输入数据depth=3，而音频信号的**梅尔频谱图**是深度为depth=1，可以认为是灰度图，输入维度(batch,1,H,W)，因此实际使用中，只需要将传统的CNN图像分类的backbone的第一层卷积层的in\_channels=1即可。需要注意的是，由于维度不一致，导致不能使用imagenet的pretrained模型。

当然可以将**梅尔频谱图(灰度图)**是转为3通道RGB图，这样就跟普通的RGB图像没有什么区别了，也可以imagenet的pretrained模型，如

>     # 将梅尔频谱图(灰度图)是转为为3通道RGB图
>     spec_image = cv2.cvtColor(spec_image, cv2.COLOR_GRAY2RGB)

def build_model(self, cfg):
            if cfg.net_type == "mbv2":
                model = mobilenet_v2.mobilenet_v2(num_classes=cfg.num_classes)
            elif cfg.net_type == "resnet34":
                model = resnet.resnet34(num_classes=args.num_classes)
            elif cfg.net_type == "resnet18":
                model = resnet.resnet18(num_classes=args.num_classes)
            else:
                raise Exception("Error:{}".format(cfg.net_type))
            model.to(self.device)
            return model

（3）训练参数配置

相关的命令行参数，可参考：

def get_parser():
        data_dir = "/media/pan/新加卷/dataset/UrbanSound8K"
        # data_dir = "E:/dataset/UrbanSound8K"
        train_data = 'data/UrbanSound8K/train.txt'
        test_data = 'data/UrbanSound8K/test.txt'
        parser = argparse.ArgumentParser(description=__doc__)
        parser.add_argument('--batch_size', type=int, default=32, help='训练的批量大小')
        parser.add_argument('--num_workers', type=int, default=4, help='读取数据的线程数量')
        parser.add_argument('--num_epoch', type=int, default=100, help='训练的轮数')
        parser.add_argument('--num_classes', type=int, default=10, help='分类的类别数量')
        parser.add_argument('--learning_rate', type=float, default=1e-3, help='初始学习率的大小')
        parser.add_argument('--input_shape', type=str, default='(None, 1, 128, 128)', help='数据输入的形状')
        parser.add_argument('--gpu_id', type=int, default=0, help='GPU ID')
        parser.add_argument('--net_type', type=str, default="mbv2", help='backbone')
        parser.add_argument('--data_dir', type=str, default=data_dir, help='数据路径')
        parser.add_argument('--train_data', type=str, default=train_data, help='训练数据的数据列表路径')
        parser.add_argument('--test_data', type=str, default=test_data, help='测试数据的数据列表路径')
        parser.add_argument('--work_dir', type=str, default='work_space/', help='模型保存的路径')
        return parser

配置好数据路径，其他参数默认设置，即可以开始训练了：

python train.py

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_20_color_FFFFFF_t_70_g_se_x_16][]

训练完成，使用mobilenet\_v2,最终训练集准确率99%左右，测试集81%左右，看起来有点过拟合了。

如果想进一步提高识别准确率可以使用更重的backbone，如resnet34,采用更多的数据增强方法，提高模型的泛发性。

完整的训练代码train.py：

# -*-coding: utf-8 -*-
    """
        @Author : panjq
        @E-mail : pan_jinquan@163.com
        @Date   : 2021-07-28 09:09:32
    """
    
    import argparse
    import os
    import numpy as np
    import torch
    import tensorboardX as tensorboard
    from datetime import datetime
    from tqdm import tqdm
    from torch.utils.data import DataLoader
    from torch.optim.lr_scheduler import StepLR, MultiStepLR
    from audio.dataloader.audio_dataset import AudioDataset
    from audio.utils.utility import print_arguments
    from audio.utils import file_utils
    from audio.models import mobilenet_v2, resnet
    
    
    class Train(object):
        """Training  Pipeline"""
    
        def __init__(self, cfg):
            self.device = "cuda:{}".format(cfg.gpu_id) if torch.cuda.is_available() else "cpu"
            self.num_epoch = cfg.num_epoch
            self.net_type = cfg.net_type
            self.work_dir = os.path.join(cfg.work_dir, self.net_type)
            self.model_dir = os.path.join(self.work_dir, "model")
            self.log_dir = os.path.join(self.work_dir, "log")
            file_utils.create_dir(self.model_dir)
            file_utils.create_dir(self.log_dir)
    
            self.tensorboard = tensorboard.SummaryWriter(self.log_dir)
            self.train_loader, self.test_loader = self.build_dataset(cfg)
            # 获取模型
            self.model = self.build_model(cfg)
            # 获取优化方法
            self.optimizer = torch.optim.Adam(params=self.model.parameters(),
                                              lr=cfg.learning_rate,
                                              weight_decay=5e-4)
            # 获取学习率衰减函数
            self.scheduler = MultiStepLR(self.optimizer, milestones=[50, 80], gamma=0.1)
            # 获取损失函数
            self.losses = torch.nn.CrossEntropyLoss()
    
        def build_dataset(self, cfg):
            """构建训练数据和测试数据"""
            input_shape = eval(cfg.input_shape)
            # 获取数据
            train_dataset = AudioDataset(cfg.train_data, data_dir=cfg.data_dir, mode='train', spec_len=input_shape[3])
            train_loader = DataLoader(dataset=train_dataset, batch_size=cfg.batch_size, shuffle=True,
                                      num_workers=cfg.num_workers)
    
            test_dataset = AudioDataset(cfg.test_data, data_dir=cfg.data_dir, mode='test', spec_len=input_shape[3])
            test_loader = DataLoader(dataset=test_dataset, batch_size=cfg.batch_size, shuffle=False,
                                     num_workers=cfg.num_workers)
            print("train nums:{}".format(len(train_dataset)))
            print("test  nums:{}".format(len(test_dataset)))
            return train_loader, test_loader
    
        def build_model(self, cfg):
            """构建模型"""
            if cfg.net_type == "mbv2":
                model = mobilenet_v2.mobilenet_v2(num_classes=cfg.num_classes)
            elif cfg.net_type == "resnet34":
                model = resnet.resnet34(num_classes=args.num_classes)
            elif cfg.net_type == "resnet18":
                model = resnet.resnet18(num_classes=args.num_classes)
            else:
                raise Exception("Error:{}".format(cfg.net_type))
            model.to(self.device)
            return model
    
        def epoch_test(self, epoch):
            """模型测试"""
            loss_sum = []
            accuracies = []
            self.model.eval()
            with torch.no_grad():
                for step, (inputs, labels) in enumerate(tqdm(self.test_loader)):
                    inputs = inputs.to(self.device)
                    labels = labels.to(self.device).long()
                    output = self.model(inputs)
                    # 计算损失值
                    loss = self.losses(output, labels)
                    # 计算准确率
                    output = torch.nn.functional.softmax(output, dim=1)
                    output = output.data.cpu().numpy()
                    output = np.argmax(output, axis=1)
                    labels = labels.data.cpu().numpy()
                    acc = np.mean((output == labels).astype(int))
                    accuracies.append(acc)
                    loss_sum.append(loss)
            acc = sum(accuracies) / len(accuracies)
            loss = sum(loss_sum) / len(loss_sum)
            print("Test epoch:{:3.3f},Acc:{:3.3f},loss:{:3.3f}".format(epoch, acc, loss))
            print('=' * 70)
            return acc, loss
    
        def epoch_train(self, epoch):
            """模型训练"""
            loss_sum = []
            accuracies = []
            self.model.train()
            for step, (inputs, labels) in enumerate(tqdm(self.train_loader)):
                inputs = inputs.to(self.device)
                labels = labels.to(self.device).long()
                output = self.model(inputs)
                # 计算损失值
                loss = self.losses(output, labels)
                self.optimizer.zero_grad()
                loss.backward()
                self.optimizer.step()
    
                # 计算准确率
                output = torch.nn.functional.softmax(output, dim=1)
                output = output.data.cpu().numpy()
                output = np.argmax(output, axis=1)
                labels = labels.data.cpu().numpy()
                acc = np.mean((output == labels).astype(int))
                accuracies.append(acc)
                loss_sum.append(loss)
                if step % 50 == 0:
                    lr = self.optimizer.state_dict()['param_groups'][0]['lr']
                    print('[%s] Train epoch %d, batch: %d/%d, loss: %f, accuracy: %f，lr:%f' % (
                        datetime.now(), epoch, step, len(self.train_loader), sum(loss_sum) / len(loss_sum),
                        sum(accuracies) / len(accuracies), lr))
            acc = sum(accuracies) / len(accuracies)
            loss = sum(loss_sum) / len(loss_sum)
            print("Train epoch:{:3.3f},Acc:{:3.3f},loss:{:3.3f}".format(epoch, acc, loss))
            print('=' * 70)
            return acc, loss
    
        def run(self):
            # 开始训练
            for epoch in range(self.num_epoch):
                train_acc, train_loss = self.epoch_train(epoch)
                test_acc, test_loss = self.epoch_test(epoch)
                self.tensorboard.add_scalar("train_acc", train_acc, epoch)
                self.tensorboard.add_scalar("train_loss", train_loss, epoch)
                self.tensorboard.add_scalar("test_acc", test_acc, epoch)
                self.tensorboard.add_scalar("test_loss", test_loss, epoch)
                self.scheduler.step()
                self.save_model(epoch, test_acc)
    
        def save_model(self, epoch, acc):
            """保持模型"""
            model_path = os.path.join(self.model_dir, 'model_{:0=3d}_{:.3f}.pth'.format(epoch, acc))
            if not os.path.exists(os.path.dirname(model_path)):
                os.makedirs(os.path.dirname(model_path))
            torch.jit.save(torch.jit.script(self.model), model_path)
    
    
    def get_parser():
        data_dir = "/media/pan/新加卷/dataset/UrbanSound8K"
        # data_dir = "E:/dataset/UrbanSound8K"
        train_data = 'data/UrbanSound8K/train.txt'
        test_data = 'data/UrbanSound8K/test.txt'
        parser = argparse.ArgumentParser(description=__doc__)
        parser.add_argument('--batch_size', type=int, default=32, help='训练的批量大小')
        parser.add_argument('--num_workers', type=int, default=4, help='读取数据的线程数量')
        parser.add_argument('--num_epoch', type=int, default=100, help='训练的轮数')
        parser.add_argument('--num_classes', type=int, default=10, help='分类的类别数量')
        parser.add_argument('--learning_rate', type=float, default=1e-3, help='初始学习率的大小')
        parser.add_argument('--input_shape', type=str, default='(None, 1, 128, 128)', help='数据输入的形状')
        parser.add_argument('--gpu_id', type=int, default=0, help='GPU ID')
        parser.add_argument('--net_type', type=str, default="mbv2", help='backbone')
        parser.add_argument('--data_dir', type=str, default=data_dir, help='数据路径')
        parser.add_argument('--train_data', type=str, default=train_data, help='训练数据的数据列表路径')
        parser.add_argument('--test_data', type=str, default=test_data, help='测试数据的数据列表路径')
        parser.add_argument('--work_dir', type=str, default='work_space/', help='模型保存的路径')
        return parser
    
    
    if __name__ == '__main__':
        parser = get_parser()
        args = parser.parse_args()
        print_arguments(args)
        t = Train(args)
        t.run()

## 5.预测demo.py ##

# -*-coding: utf-8 -*-
    """
        @Author : panjq
        @E-mail : pan_jinquan@163.com
        @Date   : 2021-07-28 09:09:32
    """
    
    import os
    import cv2
    import argparse
    import librosa
    import torch
    import numpy as np
    from audio.dataloader.audio_dataset import load_audio, normalization
    from audio.dataloader.record_audio import record_audio
    from audio.utils import file_utils, image_utils
    
    
    class Predictor(object):
        def __init__(self, cfg):
            # self.device = "cuda:{}".format(cfg.gpu_id) if torch.cuda.is_available() else "cpu"
            self.device = "cpu"
            self.input_shape = eval(cfg.input_shape)
            self.spec_len = self.input_shape[3]
            self.model = self.build_model(cfg.model_file)
    
        def build_model(self, model_file):
            # 加载模型
            model = torch.jit.load(model_file, map_location="cpu")
            model.to(self.device)
            model.eval()
            return model
    
        def inference(self, input_tensors):
            with torch.no_grad():
                input_tensors = input_tensors.to(self.device)
                output = self.model(input_tensors)
            return output
    
        def pre_process(self, spec_image):
            """音频数据预处理"""
            if spec_image.shape[1] > self.spec_len:
                input = spec_image[:, 0:self.spec_len]
            else:
                input = np.zeros(shape=(self.spec_len, self.spec_len), dtype=np.float32)
                input[:, 0:spec_image.shape[1]] = spec_image
            input = normalization(input)
            input = input[np.newaxis, np.newaxis, :]
            input_tensors = np.concatenate([input])
            input_tensors = torch.tensor(input_tensors, dtype=torch.float32)
            return input_tensors
    
        def post_process(self, output):
            """输出结果后处理"""
            scores = torch.nn.functional.softmax(output, dim=1)
            scores = scores.data.cpu().numpy()
            # 显示图片并输出结果最大的label
            label = np.argmax(scores, axis=1)
            score = scores[:, label]
            return label, score
    
        def detect(self, audio_file):
            """
            :param audio_file: 音频文件
            :return: label:预测音频的label
                     score: 预测音频的置信度
            """
            spec_image = load_audio(audio_file)
            input_tensors = self.pre_process(spec_image)
            # 执行预测
            output = self.inference(input_tensors)
            label, score = self.post_process(output)
            return label, score
    
        def detect_file_dir(self, file_dir):
            """
            :param file_dir: 音频文件目录
            :return:
            """
            file_list = file_utils.get_files_lists(file_dir, postfix=["*.wav"])
            for file in file_list:
                print(file)
                label, score = self.detect(file)
                print(label, score)
    
        def detect_record_audio(self, audio_dir):
            """
            :param audio_dir: 录制音频并进行识别
            :return:
            """
            time = file_utils.get_time()
            file = os.path.join(audio_dir, time + ".wav")
            record_audio(file)
            label, score = self.detect(file)
            print(file)
            print(label, score)
    
    
    def get_parser():
        model_file = 'data/pretrained/model_060_0.827.pth'
        file_dir = 'data/audio'
        parser = argparse.ArgumentParser(description=__doc__)
        parser.add_argument('--num_classes', type=int, default=10, help='分类的类别数量')
        parser.add_argument('--input_shape', type=str, default='(None, 1, 128, 128)', help='数据输入的形状')
        parser.add_argument('--net_type', type=str, default="mbv2", help='backbone')
        parser.add_argument('--gpu_id', type=int, default=0, help='GPU ID')
        parser.add_argument('--model_file', type=str, default=model_file, help='模型文件')
        parser.add_argument('--file_dir', type=str, default=file_dir, help='音频文件的目录')
        return parser
    
    
    if __name__ == '__main__':
        parser = get_parser()
        args = parser.parse_args()
        p = Predictor(args)
        p.detect_file_dir(file_dir=args.file_dir)
        # audio_dir = 'data/record_audio'
        # p.detect_record_audio(audio_dir=audio_dir)

完整的项目代码：[https://download.csdn.net/download/guyuealian/30306697][https_download.csdn.net_download_guyuealian_30306697]

--------------------

更多AI博客，请参考：

人体关键点检测需要用到人体检测，请查看鄙人另一篇博客：[2D Pose人体关键点实时检测(Python/Android /C++ Demo)\_pan\_jinquan的博客-CSDN博客][2D Pose_Python_Android _C_ Demo_pan_jinquan_-CSDN]

![20210723091643896.gif][]

![watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_20_color_FFFFFF_t_70_g_se_x_16 1][]

[https_download.csdn.net_download_guyuealian_30306697]: https://download.csdn.net/download/guyuealian/30306697
[https_panjinquan.blog.csdn.net_article_details_120601437]: https://panjinquan.blog.csdn.net/article/details/120601437
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_10_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/867bb69a18e648adaf1b51aa72d1bf06.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_13_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/3ec85be2f48b473889c303635dfc30e3.png
[https_zenodo.org_record_1203745_files_UrbanSound8K.tar.gz]: https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz
[librosa]: https://www.cnblogs.com/xingshansi/p/6816308.html
[mel spectrogram]: https://zhuanlan.zhihu.com/p/351956040
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220828/e826746eb1384c39ad41503e50b74838.png
[2D Pose_Python_Android _C_ Demo_pan_jinquan_-CSDN]: https://panjinquan.blog.csdn.net/article/details/115765863
[20210723091643896.gif]: /images/20220828/07c5063806b84d5ab6c9c785f4a84040.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBAcGFuX2ppbnF1YW4_size_20_color_FFFFFF_t_70_g_se_x_16 1]: /images/20220828/368b3a5dd2d94ff483bef401d82f2f61.png