继我之前的问题之后,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, 0, buffer.Length); short[] sampleBuffer = new short[read/2]; Buffer.BlockCopy(buffer, 0, sampleBuffer, 0, 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音频中DTMF音的存在和值文件.
我到处寻找答案,包括这个答案中引用的库; 遗憾的是,此处的代码似乎不起作用(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, 0, buffer.Length); int bufferPos = 0; while (bufferPos < buffer.Length-(sampleSize*2)) { short[] sampleBuffer = new short[sampleSize]; Buffer.BlockCopy(buffer, bufferPos, sampleBuffer, 0, 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的答案:1
按下了:
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AdjustmentFactor
超过1.2,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:上面的测试文件可以在这里找到.在adjustedMeanPower
上面的例子是47.6660450354638
,与功率是:
CalculateGoertzel()
返回所提供样本中所选频率的功率.
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,应该足以计算所有Goerzel值的平均值,将平均值乘以一个因子(例如2或3),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, 770.0, 852.0, 941.0, 1209.0, 1336.0, 1477.0}; var powers = frequencies.Select(f => new { Frequency = f, Power = CalculateGoerzel(sample, f, 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. }
开始用AdjustmentFactor
的1.0
.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, Power = CalculateGoertzel(sampleBuffer, 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.我只是用作100.0
门槛:
if (highestPowers.All(result => result.Power > 100.0)) { ... }
我把样本量翻了一倍(我相信你用过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> { {1209, new Dictionary {{1477, "?"}, {1336, "?"}, {1209, "?"}, {941, "*"}, {852, "7"}, {770, "4"}, {697, "1"}}}, {1336, new Dictionary {{1477, "?"}, {1336, "?"}, {1209, "?"}, {941, "0"}, {852, "8"}, {770, "5"}, {697, "2"}}}, {1477, new Dictionary {{1477, "?"}, {1336, "?"}, {1209, "?"}, {941, "#"}, {852, "9"}, {770, "6"}, {697, "3"}}}, { 941, new Dictionary {{1477, "#"}, {1336, "0"}, {1209, "*"}, {941, "?"}, {852, "?"}, {770, "?"}, {697, "?"}}}, { 852, new Dictionary {{1477, "9"}, {1336, "8"}, {1209, "7"}, {941, "?"}, {852, "?"}, {770, "?"}, {697, "?"}}}, { 770, new Dictionary {{1477, "6"}, {1336, "5"}, {1209, "4"}, {941, "?"}, {852, "?"}, {770, "?"}, {697, "?"}}}, { 697, new Dictionary {{1477, "3"}, {1336, "2"}, {1209, "1"}, {941, "?"}, {852, "?"}, {770, "?"}, {697, "?"}}} }
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.你无法直接将目标频率传递给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205
按sampleSize
顺序使用,以尽量减少错误.
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(float
范围[-1.0; 1.0]中的s).我也CalculateGoertzel()
从头开始重写.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.有没有更多的假阳性,当我运行您的测试数据.我强烈建议你看看它:http://pastebin.com/serxw5nG
更新#4
我创建了一个GitHub项目和两个NuGet包来检测实时(捕获)音频和预先录制的音频文件中的DTMF音调.