音乐雷达 shazam算法

Music identification apps seem like magic at first, but underneath the hood is a sophisticated algorithm that can find songs in an instant. Here’s how they work.

音乐识别应用程序乍一看似乎很神奇，但其背后是一种复杂的算法，可以立即找到歌曲。 这是他们的工作方式。

音乐识别的魔力 (The Magic of Music Identification)

It’s probably happened to all of us. You’re having dinner at a nice restaurant, hanging out at a coffee shop, or walking around in a store, when you suddenly hear a great song playing over the speakers. Maybe it’s a song you’ve listened to before or a track you’ve never heard. So, you pull out your phone, open Shazam, and hold up your device to the ceiling. In just a flash, the app tells you what the song is, who the artist is, and where to stream it.

这可能发生在我们所有人身上。 当您突然听到一首很棒的歌曲在扬声器上播放时，您正在一家不错的餐厅吃晚餐，在咖啡厅闲逛或在商店里走来走去。 也许这是您之前听过的歌曲，或是从未听过的曲目。 因此，您拔出手机，打开Shazam，然后将设备举到天花板上。 瞬间，该应用程序就会告诉您这首歌是什么，歌手是谁以及在哪里播放。

They’re quick, remarkably accurate, and can identify even the most obscure of songs. In a nutshell, they work by isolating the song out of a recording and searching it against an expansive database of tracks. But the technology behind how they do this is quite complex and impressive.

它们快速，非常准确，甚至可以识别最晦涩的歌曲。 简而言之，它们的工作方式是将歌曲从录音中分离出来，并在广阔的曲目数据库中进行搜索。 但是，他们执行此操作的技术非常复杂且令人印象深刻。

You might be shocked to know that the Shazam app that we know today was released way back in 2002, and the system was just as accurate and quick then as it is now. That’s all thanks to a unique algorithm that would revolutionize the music world.

您可能会感到震惊，因为我们今天所知道的Shazam应用程序早在2002年就已发布，而且该系统与现在一样准确，快捷。 这全都归功于独特的算法，它将彻底改变音乐世界。

不只是歌词 (It’s Not Just the Lyrics)

At first glance, music identification apps like Shazam may seem simple. You might think they just listen to the lyrics, the same as any voice assistant, and search it in a database of song lyrics to tell you what the song is.

乍一看，像Shazam这样的音乐识别应用程序可能看起来很简单。 您可能会认为他们就像任何语音助手一样，都只听歌词，然后在歌曲歌词数据库中进行搜索以告诉您歌曲是什么。

However, most music identification apps are capable of telling what the title of an instrumental is, or even the singer of a cover song. That’s because, instead of analyzing the lyrics of the track, they’re looking for “fingerprints” that are unique to each song in their extensive databases.

但是，大多数音乐识别应用程序都能够分辨器乐的名称，甚至是翻唱歌曲的歌手。 这是因为，他们没有在分析曲目的歌词，而是在广泛的数据库中寻找每首歌曲特有的“指纹”。

指纹技术 (Fingerprinting Technology)

You likely have devices that can be unlocked using your fingerprint, which is the arrangement of the small lines on your finger that are unique to you. Similarly, when you hold up your microphone to record a brief clip of a song, this clip gets turned into patterns of data that Shazam or another app can look up in their database.

您可能拥有可以使用指纹来解锁的设备，这是手指上唯一的细线排列。 同样，当您举起麦克风录制歌曲的简短片段时，该片段会变成Shazam或其他应用可以在其数据库中查找的数据模式。

At first glance, that method seems prone to several problems. Most of the time that you hear music in public, there’s background noise and distortion caused by the speakers, which can make songs unidentifiable or result in inaccurate matches. Also, there’s a lot of data captured in even a brief sound clip, which can make searching for these patterns across a database of millions of songs slow.

乍一看，该方法似乎容易出现几个问题。 在大多数情况下，您在公共场合听音乐时，背景噪声和扬声器引起的失真会导致歌曲无法识别或匹配不准确。 此外，即使是简短的声音片段，也会捕获大量数据，这会使在数百万首歌曲的数据库中搜索这些模式的速度变慢。

In an interview with Scientific American in 2003, Avery Li-Chun Wang, the chief data scientist and co-founder of Shazam, explains how their algorithm fixes these issues. The information of an audio clip can be visualized with a 3D chart known as a spectrogram, which represents a change in frequencies over a period of time. It also takes into account amplitude, which is how loud a sound is. This is represented in a spectrogram using the intensity of color.

Shazam的首席数据科学家兼联合创始人王艾利(Avery Li-Chun Wang)在2003年接受《科学美国人》采访时，解释了他们的算法如何解决这些问题。 可以使用称为频谱图的3D图表来可视化音频剪辑的信息，该图表表示一段时间内频率的变化。 它还考虑到振幅，即声音的音量。 这在频谱图中使用颜色的强度表示。

In the same way that humans cannot perceive sound unless they are at a particular frequency, instead of taking the entirety of a song into account when performing a search, Shazam only takes in “peaks,” which is the highest energy content within an audio clip. The fingerprints it captures only take in the highest frequency points within a given time frame and then the peak amplitude spots within those frequencies.

就像人类只有在特定频率下才能听见声音一样，Shazam只会吸收“峰值”，这是音频片段中的最高能量含量，而不是在执行搜索时将歌曲的整体考虑在内。 它捕获的指纹仅在给定时间范围内占据最高频率点，然后在这些频率内达到峰值振幅点。

In a research paper for Columbia University, Wang stated that the method allows them to take out most of the unnecessary parts of an audio clip like background noise and to clear up distortion. It also makes the size of the prints small enough that it takes mere milliseconds to identify a song among their vast database.

Wang在哥伦比亚大学的一份研究论文中说，这种方法可以使他们消除音频剪辑中的大部分不必要部分，例如背景噪声并清除失真。 它还使印刷品的尺寸足够小，仅需几毫秒即可在其庞大的数据库中识别歌曲。

沙赞的影响 (Shazam’s Impact)

Aside from being helpful for average listeners who hear a song they like, music identification apps also help shape the music world.

音乐识别应用程序不仅对听他们喜欢的歌曲的普通听众有帮助，而且还有助于塑造音乐世界。

Radio stations and streaming services often use the data regarding what people are Shazam-ing the most to figure out what tracks are being listened to by the public. This is helpful because it indicates a song’s catchiness and potential popularity, regardless of the artist. When you identify a song with the app, you’ll immediately see how many people have also tried to identify it.

广播电台和流媒体服务通常使用有关“人们最想做什么”的数据，以确定公众正在收听哪些曲目。 这很有用，因为它表明一首歌的流行性和潜在的受欢迎程度，与艺术家无关。 当您使用该应用程序识别歌曲时，您将立即看到有多少人也试图识别该歌曲。

Since the rise of Shazam, a handful of competitors have also popped up. Soundhound claims to be able to identify a song simply by you singing or humming to it, with mixed results. There’s also a song identifier integrated with voice apps such as Google Assistant that work very similarly to Shazam’s system.

自从Shazam崛起以来，少数竞争对手也出现了。 Soundhound声称能够仅通过唱歌或哼唱就能识别出一首歌曲，而且结果复杂。 还有一个与语音应用程序(例如Google Assistant)集成的歌曲标识符，其功能与Shazam的系统非常相似。

翻译自: https://www.howtogeek.com/659614/how-do-music-identification-apps-like-shazam-work/

音乐雷达 shazam算法