How does shazam work

Thank you for being thoughtful n for sharing your research. Keep up good work! Beautifully written article that ensures the reader has foundations explained before entering the more complex subject matter. I wish you had been my teacher; I had experts who could not simplify their understanding to help others join the adventure. Great article! Then, you can find some code generators where you put the frequencies you want to keep and it generates the code of the function that will filter the frequencies.

You made a fantastic work, because usually scientific articles are very hard to understand if you are not a scientist. Thank you. Very nice article. Not even after converting mp3 to PCM first. Headphones jack are just electrical connection to the speakers inside them, and the audio signal sent to them must be analog.

Are there any approaches to identify the same music piece play by different players? A very well written article which gives a through technical explanation. My message to yourself is how can I beat Shazam. The reason for this is to stop cheating during a quiz when playing a music clip. This may fool Shazam and stop any cheating. If I have misunderstood your article I am sorry , but advances in technology are not always a benefit to everyone. This is an incredible article way out of my league, but I read through some and skimmed through some and even without understanding the complicated math at all!

Haha I have a basic understanding of how Shazam works. Thank you for putting the time into this! Thank you so much. Thanks for the explanation. I was always wondering how shazam found the results so fast! Thanks for the refreshment, really enjoyed it! Amazing work. So much information to digest. Good job! Terrific article. Very nice! Can you explain? In example we remain songs we maximum target zone.

Hi, I mean that there must be a minimum threshold. If the number of target zones is below this thresold then the record is discarded.

Hello, thanks for the good explanation. I am curious about the complexity of the algorithm. Audio Fingerprinting is the key algorithm Shazam uses. Anyone who interested in it can refer to it. Thank you so very much for this. You explained this well, at least the part that I read. I have a programing project to do on audio fingerprinting and you will be referenced, thank you again.

Hi, I am not a coder or musical, so I apologise if my question is uninformed. Basically I was wondering, once you had an audio fingerprint and you were searching for a match, if for example, you stopped the search when you found the 10 most relevant songs to that match, are those 10 songs going to audibly sound alike? Shazam looks for perfect matches in a noisy environment. A close match could be: — if 2 songs have the exact same piano partition and this piano is the loudest instrument in both songs — 2 songs with a slight modification like more instruments in one of them — The sample you recorded with your phone is in different songs but the rest of the songs is different.

Home »» Algorithm »» How does Shazam work. Contents 1 Music and physics 1. Most reacted comment. Hottest comment thread. Recent comment authors. October 6, pm. March 21, pm. March 28, pm. January 29, am. Robin Vaughan. December 30, am. November 19, pm. October 22, am. October 4, pm. October 7, pm. September 22, am. September 8, pm. June 15, pm. June 14, pm. June 2, am. May 29, pm. Brilliant Christophe.

Thanks for taking time to enlighten some of us from the process world. April 26, pm. Very well described. Clear presentation of FFT theory. April 3, am. Amit Bhola. January 12, pm. December 15, pm. December 4, pm. August 2, pm. Mithilesh Vaidya. June 16, pm. Kunduz Baryktabasova. June 24, am. The article is nice.

May 30, pm. April 28, pm. Jonathan Taylor. April 11, pm. April 7, pm. April 6, pm. March 17, am. Stefan Teau. January 23, pm. January 25, pm. What an amazing article! Thanks for doing all this hard work and sharing it with the community. January 8, pm.

January 10, pm. December 16, am. December 16, pm. October 31, pm. October 28, am. October 26, pm. Really liked it. The EE part I will have to read more to grasp a better understanding. October 14, pm. Wow…youve put so much efforts into making it work.. Brilliant work indeed. September 28, am. Sumit Panwar.

You explained it so well. Covers a range of learning. Thanks for writing up your work!!! September 20, am. Paul Rudman. September 6, pm. Kenneth Graham. August 22, pm. DJ EmCee Free. August 20, am. May 3, am. Mary Jane Suazana. April 15, pm. Great explanation! January 21, pm. January 8, am. Mike Smith. December 24, pm. A fantastic article from an amazing mind. You are to be congratulated!

December 23, am. December 10, am. November 1, am. November 1, pm. Thank you, Christophe, for your amazing work to facilitate understanding. June 21, am. Keith ferguson. If I have misunderstood your article I am sorry , but advances in technology are not always a benefit to everyone Thanks. April 20, pm. Can u explain what are logarithmic bands and how should they look? March 11, am. January 1, pm. December 17, am. December 3, pm. November 25, am. November 4, pm.

November 3, pm. October 18, pm. October 16, pm. September 12, pm. August 26, pm. August 19, pm. August 8, am. August 7, am. July 27, am. Hi, amazing article Can you explain? July 27, pm. July 21, pm. Shazam uses song recordings free from background noise and distortion to create fingerprints for its database.

When you record a song with the app in a noisy place, it creates an audio fingerprint of your recording by identifying the notes with the highest energy on the recording. It then searches its database for a match for your recording's audio fingerprints, provided that the background noise level was not high enough to distort the data used to create the audio fingerprint.

Shazam is great at matching songs, even obscure music you think it might not have in its database. But are there moments when Shazam can't identify a track? When you Shazam a song in a place where the background noise level is too high, the noise distorts the data on the Spectrogram.

Because of that, the audio fingerprint of your recording will be different from that of the original song. When that happens, Shazam returns the Song not Known dialogue because it cannot find a match for the audio fingerprint.

Shazam falls short in its ability to identify music from live performances. This is because the audio you record in live performances often differs from the original version of the song Shazam uses to create audio fingerprints.

The only way Shazam can identify a song during a live performance is if the band is skilled enough to perform the song exactly as it was recorded. Good luck with the band trying to do that…. The Shazam algorithm can only identify prerecorded music.

When you hum a song, Shazam creates a fingerprint for it. But because a hum is only an attempt to resynthesize a song, the algorithm will fail to match the recording.

Therefore, instead of a single conversion, an analog-to-digital converter performs many conversions on very small pieces of the signal - a process known as sampling. The Nyquist-Shannon Theorem tells us what sampling rate is necessary to capture a certain frequency in continuous signal.

In particular, to capture all of the frequencies that a human can hear in an audio signal, we must must sample the signal at a frequency twice that of the human hearing range. The human ear can detect frequencies roughly between 20 Hz and 20, Hz. As a result, audio is most often recorded at a sampling rate of 44, Hz. This specific rate was originally chosen by Sony because it could be recorded on modified video equipment running at either 25 frames per second PAL or 30 frames per second using an NTSC monochrome video recorder and cover the 20, Hz bandwidth thought necessary to match professional analog recording equipment of the time.

So, when choosing the frequency of the sample that is needed to be recorded you will probably want to go with 44, Hz. Recording a sampled audio signal is easy. Since modern sound cards already come with analog-to-digital converters, just pick a programming language, find an appropriate library, set the frequency of the sample, number of channels typically mono or stereo , sample size e. Then open the line from your sound card just like any input stream, and write to a byte array.

Here is how that can be done in Java:. Just read the data from TargetDataLine. In this example, the running flag is a global variable which is stopped by another thread - for example, if we have GUI with the STOP button. What we have in this byte array is signal recorded in the time domain. The time-domain signal represents the amplitude change of the signal over time. In the early s, Jean-Baptiste Joseph Fourier made the remarkable discovery that any signal in the time domain is equivalent to the sum of some possibly infinite number of simple sinusoidal signals, given that each component sinusoid has a certain frequency, amplitude, and phase.

The series of sinusoids that together form the original time-domain signal is known as its Fourier series. In other words, it is possible to represent any time domain signal by simply giving the set of frequencies, amplitudes, and phases corresponding to each sinusoid that makes up the signal. This representation of the signal is known as the frequency domain.

In some ways, the frequency domain acts as a type of fingerprint or signature for the time-domain signal, providing a static representation of a dynamic signal. The following animation demonstrates the Fourier series of a 1 Hz square wave , and how an approximate square wave can be generated out of sinusoidal components.

The signal is shown in the time domain above, and the frequency domain below. Analyzing a signal in the frequency domain simplifies many things immensely. It is more convenient in the world of digital signal processing because the engineer can study the spectrum the representation of the signal in the frequency domain and determine which frequencies are present, and which are missing.

After that, one can do filtering, increase or decrease some frequencies, or just recognize the exact tone from the given frequencies. So we need to find a way to convert our signal from the time domain to the frequency domain. The DFT is a mathematical methodology for performing Fourier analysis on a discrete sampled signal.

It converts a finite list of equally spaced samples of a function into the list of coefficients of a finite combination of complex sinusoids, ordered by their frequencies, by considering if those sinusoids had been sampled at the same rate.

Below is an example of an FFT function written in Java. FFT takes complex numbers as input. One unfortunate side effect of FFT is that we lose a great deal of information about timing. Although theoretically this can be avoided, the performance overheads are enormous. But this is the key information that makes the song what it is! Somehow we need to at know what point of time each frequency appeared. The size of each chunk can be determined in a few different ways. If we pick 4 kB for the size of a chunk, we will have 44 chunks of data to analyze in every second of the song.

In the inner loop we are putting the time-domain data the samples into a complex number with imaginary part 0. In the outer loop, we iterate through all the chunks and perform FFT analysis on each. Once we have information about the frequency makeup of the signal, we can start forming our digital fingerprint of the song.

This is the most important part of the entire Shazam audio recognition process.