c# – 从WAV文件解码DTMF

从我的 earlier question开始,我的目标是从C#中检测WAV文件中的DTMF音调.但是,我真的很难理解如何做到这一点.

我知道DTMF使用频率组合,并且可以使用Goertzel算法……不知何故.我抓住了一个Goertzel代码片段,我尝试将.WAV文件推入其中(使用NAudio读取文件,这是一个8KHz单声道16位PCM WAV)：

using (WaveFileReader reader = new WaveFileReader(@"dtmftest_w.wav"))
  {
      byte[] buffer = new byte[reader.Length];

      int read = reader.Read(buffer,buffer.Length);
      short[] sampleBuffer = new short[read/2];
      Buffer.BlockCopy(buffer,sampleBuffer,read/2);
      Console.WriteLine(CalculateGoertzel(sampleBuffer,8000,16));                 
   }

 public static double CalculateGoertzel(short[] sample,double frequency,int samplerate)
   {
      double Skn,Skn1,Skn2;
      Skn = Skn1 = Skn2 = 0;
      for (int i = 0; i < sample.Length; i++)
         {
            Skn2 = Skn1;
            Skn1 = Skn;
            Skn = 2 * Math.Cos(2 * Math.PI * frequency / samplerate) * Skn1 - Skn2 + sample[i];
         }
      double WNk = Math.Exp(-2 * Math.PI * frequency / samplerate);
      return 20 * Math.Log10(Math.Abs((Skn - WNk * Skn1)));
    }

我知道我在做什么是错的：我假设我应该遍历缓冲区,并且一次只计算一小块的Goertzel值 – 这是正确的吗？

其次,我真的不明白Goertzel方法的输出告诉我的是：我得到一个双(例如：210.985812)返回,但我不知道将其等同于DTMF音的存在和值.音频文件.

我到处寻找答案,包括 this答案中引用的图书馆;遗憾的是,代码 here似乎不起作用(如网站上的评论中所述). TAPIEx提供商业图书馆;我已经尝试了他们的评估库,它完全符合我的需要 – 但他们没有回复电子邮件,这让我对实际购买他们的产品持谨慎态度.

当我可能不知道确切的问题时,我非常清楚我正在寻找答案,但最终我需要的是一种在.WAV文件中找到DTMF音调的方法.我是在正确的路线,如果没有,有人能指出我正确的方向吗？

编辑：使用@Abbondanza的代码作为基础,并且(可能是从根本上错误的)假设我需要滴入音频文件的小部分,我现在有了这个(非常粗略,只有概念验证) )代码：

const short sampleSize = 160;

using (WaveFileReader reader = new WaveFileReader(@"\\mac\home\dtmftest.wav"))
        {           
            byte[] buffer = new byte[reader.Length];

            reader.Read(buffer,buffer.Length);

            int bufferPos = 0;

            while (bufferPos < buffer.Length-(sampleSize*2))
            {
                short[] sampleBuffer = new short[sampleSize];
                Buffer.BlockCopy(buffer,bufferPos,sampleSize*2);


                var frequencies = new[] {697.0,770.0,852.0,941.0,1209.0,1336.0,1477.0};

                var powers = frequencies.Select(f => new
                {
                    Frequency = f,Power = CalculateGoertzel(sampleBuffer,f,8000)              
                });

                const double AdjustmentFactor = 1.05;
                var adjustedMeanPower = AdjustmentFactor*powers.Average(result => result.Power);

                var sortedPowers = powers.OrderByDescending(result => result.Power);
                var highestPowers = sortedPowers.Take(2).ToList();

                float seconds = bufferPos / (float)16000;

                if (highestPowers.All(result => result.Power > adjustedMeanPower))
                {
                    // Use highestPowers[0].Frequency and highestPowers[1].Frequency to 
                    // classify the detected DTMF tone.

                    switch (Convert.ToInt32(highestPowers[0].Frequency))
                    {
                        case 1209:
                            switch (Convert.ToInt32(highestPowers[1].Frequency))
                            {
                                case 697:
                                    Console.WriteLine("1 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 770:
                                    Console.WriteLine("4 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 852:
                                    Console.WriteLine("7 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 941:
                                    Console.WriteLine("* pressed at " + bufferPos);
                                    break;
                            }
                            break;
                        case 1336:
                            switch (Convert.ToInt32(highestPowers[1].Frequency))
                            {
                                case 697:
                                    Console.WriteLine("2 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 770:
                                    Console.WriteLine("5 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 852:
                                    Console.WriteLine("8 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 941:
                                    Console.WriteLine("0 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                            }
                            break;
                        case 1477:
                            switch (Convert.ToInt32(highestPowers[1].Frequency))
                            {
                                case 697:
                                    Console.WriteLine("3 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 770:
                                    Console.WriteLine("6 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 852:
                                    Console.WriteLine("9 pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                                case 941:
                                    Console.WriteLine("# pressed at " + bufferPos + " (" + seconds + "s)");
                                    break;
                            }
                            break;
                    }
                }
                else
                {
                    Console.WriteLine("No DTMF at " + bufferPos + " (" + seconds + "s)");
                }
                bufferPos = bufferPos + (sampleSize*2);
            }

这是在Audacity中查看的示例文件;我添加了按下的DTMF按键 –

而且…它几乎可以工作.从上面的文件中,我几乎不会看到任何DTMF,但是,我的代码报告：

9 pressed at 1920 (0.12s)
1 pressed at 2880 (0.18s)
* pressed at 3200
1 pressed at 5120 (0.32s)
1 pressed at 5440 (0.34s)
7 pressed at 5760 (0.36s)
7 pressed at 6080 (0.38s)
7 pressed at 6720 (0.42s)
5 pressed at 7040 (0.44s)
7 pressed at 7360 (0.46s)
7 pressed at 7680 (0.48s)
1 pressed at 8000 (0.5s)
7 pressed at 8320 (0.52s)

…直到它达到3秒,然后它开始稳定到正确答案：按下1：

7 pressed at 40000 (2.5s)
# pressed at 43840 (2.74s)
No DTMF at 44800 (2.8s)
1 pressed at 45120 (2.82s)
1 pressed at 45440 (2.84s)
1 pressed at 46080 (2.88s)
1 pressed at 46720 (2.92s)
4 pressed at 47040 (2.94s)
1 pressed at 47360 (2.96s)
1 pressed at 47680 (2.98s)
1 pressed at 48000 (3s)
1 pressed at 48960 (3.06s)
4 pressed at 49600 (3.1s)
1 pressed at 49920 (3.12s)
1 pressed at 50560 (3.16s)
1 pressed at 51520 (3.22s)
1 pressed at 52160 (3.26s)
4 pressed at 52480 (3.28s)

如果我将AdjustmentFactor提升到1.2以上,我几乎得不到任何检测.

我觉得我差不多了,但是有谁能看到我错过了什么？

EDIT2：上面的测试文件可用here.上例中的adjustedMeanPower为47.6660450354638,权限为：

解决方法

CalculateGoertzel()返回所提供样本中所选频率的功效.

计算每个DTMF频率(697,770,852,941,1209,1336和1477 Hz)的此功率,对得到的功率进行排序并选择最高的两个.如果两者都高于某个阈值,则检测到DTMF音调.

您用作阈值的方法取决于样品的信噪比(SNR).首先,计算所有Goerzel值的平均值,将平均值乘以一个因子(例如2或3)就足够了,并检查两个最高的Goerzel值是否高于该值.

这是一个代码片段,以更正式的方式表达我的意思：

var frequencies = new[] {697.0,1477.0};

var powers = frequencies.Select(f => new
{
    Frequency = f,Power = CalculateGoerzel(sample,samplerate)
});

const double AdjustmentFactor = 1.0;
var adjustedMeanPower = AdjustmentFactor * powers.Average(result => result.Power);

var sortedPowers = powers.OrderByDescending(result => result.Power);
var highestPowers = sortedPowers.Take(2).ToList();

if (highestPowers.All(result => result.Power > adjustedMeanPower))
{
    // Use highestPowers[0].Frequency and highestPowers[1].Frequency to 
    // classify the detected DTMF tone.
}

从adjustFactor 1.0开始.如果您从测试数据中得到误报(即您在不应该有任何DTMF音调的样本中检测到DTMF音调),请继续增加它直到误报停止.

更新#1

我在wave文件上尝试了你的代码并调整了一些东西：

在Goertzel计算之后,我实现了可枚举(对性能很重要)：

var powers = frequencies.Select(f => new
{
    Frequency = f,8000)
// Materialize enumerable to avoid multiple calculations.
}).ToList();

我没有使用调整后的平均值进行阈值处理.我只使用100.0作为阈值：

if (highestPowers.All(result => result.Power > 100.0))
{
     ...
}

我把样本量增加了一倍(我相信你使用了160)：

int sampleSize = 160 * 2;

我修复了你的DTMF分类.我使用嵌套字典来捕获所有可能的情况：

var phoneKeyOf = new Dictionary<int,Dictionary<int,string>>
{
    {1209,new Dictionary<int,string> {{1477,"?"},{1336,{1209,{941,"*"},{852,"7"},{770,"4"},{697,"1"}}},"0"},"8"},"5"},"2"}}},{1477,"#"},"9"},"6"},"3"}}},{ 941,"?"}}},{ 852,{ 770,{ 697,"3"},"2"},"1"},"?"}}}
}

然后检索电话密钥：

var key = phoneKeyOf[(int)highestPowers[0].Frequency][(int)highestPowers[1].Frequency];

结果并不完美,但有些可靠.

更新#2

我想我已经找到了问题,但现在不能自己试试.您无法将目标频率直接传递给CalculateGoertzel().必须将其标准化为以DFT箱为中心.在计算权力时尝试这种方法：

var powers = frequencies.Select(f => new
{
    Frequency = f,// Pass normalized frequenzy
    Power = CalculateGoertzel(sampleBuffer,Math.Round(f*sampleSize/8000.0),8000)
}).ToList();

此外,您必须使用205作为sampleSize,以最大限度地减少错误.

更新#3

我重新编写了原型以使用NAudio的ISampleProvider接口,该接口返回标准化的样本值(浮点范围[-1.0; 1.0]).我还从头开始重写了CalculateGoertzel().它仍然没有经过性能优化,但在频率之间提供了更多,更明显的功率差异.当我运行你的测试数据时,没有更多的误报.我强烈建议你看看它：http://pastebin.com/serxw5nG

更新#4

我创建了一个GitHub project和two NuGet packages来检测实时(捕获)音频和预先录制的音频文件中的DTMF音调.

原文链接：https://www.f2er.com/csharp/244913.html

c# – 从WAV文件解码DTMF

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