识别声音是男性还是女性
我对音频工程不太感兴趣，所以请对我宽容一些。我正在接收音频文件作为输入，并且需要检测说话者是男性还是女性。有什么想法如何去做这件事吗？
我正在使用 php，但我愿意使用其他语言，并且不介意学习一点合理的理论，只要时间与任务相称即可。
I'm not much into audio engineering, so please be easy on me. I'm receiving an audio file as input, and need to detect whether the speaker is male or female. Any ideas how to go about doing this?
I'm using php, but am open to using other languages, and don't mind learning a little bit of sound theory as long as the time is proportionate to the task.
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我无法真正提供对此问题的具体见解，但我首先阅读以下文章： 性别从语音分类。
这至少应该给出所涉及的概念/方法的想法（据我所知，本文对此进行了很好的描述）。
I can't really provide specific insight to this problem , but I'd start by reading the following article: Gender Classification from Speech.
That should at least give an idea of the concepts / methodologies involved (this article describes this quite well as far as I can tell).
首先，您必须找到音调值，可以在本文中找到一种用于查找语音音调值的出色算法：http://www.fon.hum.uva.nl/paul/papers/Proceedings_1993.pdf 。
它非常准确。
First of all you will have to find pitch values and one great algorithm for finding pitch values for voice can be find on this article: http://www.fon.hum.uva.nl/paul/papers/Proceedings_1993.pdf .
It's amazingly accurate.
我同意克里斯托夫，因为我在这方面没有太多经验，而且认为一些研究将是你最好的道路。
如果我必须尝试一下，我猜这将涉及使用傅立叶变换计算样本的频谱，然后找出平均频率所在的位置。针对不同文化和语言建立大量男性与女性样本，然后将特定样本的平均频率与已建立的男性与女性平均值进行比较。
但我可能完全错了，所以研究确实是你最好的选择。
I'm with Christophe, both in that I don't have too much experience with this and also think some research would be your best path.
If I had to take a stab at this though, I would guess that it would involve computing the frequency spectrum of the sample using Fourier transforms, and then figuring out where the mean frequency lay. Build up a large sample of male vs female, for different cultures and languages, and then compare your specific sample's mean frequency to established means for male vs female.
I could be completely wrong though, so research is really your best bet.
一种方法是使用人工神经网络。您为神经网络提供了一些训练示例，希望它能够学会正确分类声音。您可能需要使用傅立叶变换进行一些特征提取，才能将数据转换为合适的形式。
如果你在谷歌上搜索“神经网络说话人识别”，有几篇关于这种方法的论文，但不幸的是我对它们不够熟悉，无法推荐任何特定的一篇。
One approach would be to use artificial neural networks. You provide the neural net with some examples for training and it should hopefully learn to correctly classify the voices. You will probably have to do some feature extraction using Fourier transforms to get the data into a suitable form.
There are several papers about this kind of approach if you search on Google for "neural network speaker identification" but unfortunately I am not familiar enough with them to recommend any particular one.