As a software engineer, I was always interested in how a computer can be taught to behave intelligently, at least on some simple tasks that we (homo-sapiens) can easily solve within frames of seconds. Basically, audio fingerprinting algorithms provide the ability to link short, unlabeled snippets of audio content to corresponding data about that content (it's all about finding unique characteristics of a song that can be later used to recall an unknown sample, or find a duplicate in the database of already processed songs). With all the advantages that the sound fingerprinting system has, there are several challenges that it has to address. Briefly, an audio fingerprint can be seen as a compressed summary of the corresponding audio object. Here in the figure, string values act as keys, mapping into hash bins (integers), which are smaller by design and much easier to compare. So, why we need to develop an entirely new system based on fingerprints, and not just use cryptographic hash functions? The framework which is going to be built (upon which the entire system will reside), will consist from several different conceptual parts. Broadly speaking, the algorithm can be logically decomposed into 2 main parts: fingerprint creation (extracting unique perceptual features from the song) and fingerprint lookup (querying the database). In terms of theoretical constructs, the Nyquist-Shannon sampling theorem states that perfect reconstruction of a signal is possible when the sample rate is greater than twice the maximum frequency of the signal being sampled, or equivalently, that the Nyquist frequency (half the sample rate) exceeds the bandwidth of the signal being sampled.
Next, if you wish to build your own method to convert a signal to single channel (mono), this method can help you out.
After preprocessing the signal to 5512 Hz PCM, the next (2) step in the audio fingerprinting algorithm is building the spectrogram of the audio input. As I've mentioned earlier, in order to get the spectrogram of the input signal, the so called Fast Fourier Transform is applied on each of the sliced frames.
The frequency domain contains exactly the same information as the time domain, just in a different form. After the FFT transform is accomplished on each slice, the output spectrogram, is cut such that 318 Hz - 2000 Hz domain is obtained (researchers decided that this domain is enough for a relevant representation of perceptual content of a file). So, getting back to the algorithm, in order to minimize the output dimensionality (each frame after FFT transform is a vector of size [0 - 1025]), the specified range of 318-2000Hz should be encoded into 32 logarithmically spaced bins (so the 2048 samples in time domain are reduced to 1025 bins in frequency domain which are then summed in 32 items in logarithmic scale).
Next, is shown the code for spectrogram creation that uses all the features discussed in previous steps.
This particular framework will use the following sign encoding: positive number - 01, negative number - 10, zero - 00.
Next, you can see an image which was transformed by using Haar decomposition (before and after).
In the above paragraphs I've described the algorithm for fingerprint creation, following you can visualize the method that performs the actual task. This method returns the actual fingerprints that will be further reduced in dimensionality through the use of Min Hash + LSH technique.
At this stage of the processing we got fingerprints that are 8192 bits log, which we would like to further reduce in their length, by creating a compact representation of each item. The Min-Hash technique works with binary vectors as follows: Select a random, but known, reordering of all the vector positions. If you wonder how the permutations were generated, I can disappoint you that the procedure is not straightforward at all. Generally stated, up until this moment we've been able to reduce the dimensionality of the initial vector through the use of different algorithms, as Fourier transform, Haar wavelet decomposition, selecting Top-T wavelets which contain the most relevant information, and Min-Hash algorithm. In audio fingerprinting algorithm discussed in this article, we use L hash functions from Min-Hash algorithm discussed in the previous section (where each hash function represents a concatenated value of B min hashes).
After the min-hashed fingerprint was partitioned into LSH tables, the process of extracting signatures from audio files can be considered completed. There are many steps required in building the fingerprints, so its not uncommon to lose the connections between all of them.
The spectrogram is logarithmized and divided into spectral images, which will be further representing the fingerprints themselves.
Each spectral-frame is transformed using Haar wavelet decomposition, and Top-200 wavelets are kept in the original image.
Finally, using Locality Sensitive Hashing, Min-Hash signature for each fingerprint is spread in 25 hash tables, which will be later used in lookup.
The application, which will use the described algorithm, is going to detect duplicate files on your local machine. Model: as in the classic MVC pattern, the model refers to either (a) an object model that represents the real state content (an object-oriented approach), or (b) the data access layer that represents that content (a data-centric approach). View: is responsible for defining the structure, layout, and appearance of what the user sees on the screen. ViewModel: it acts as an intermediary between the View and the Model, and is responsible for handling the view logic. XMPlay is a free and lightweight audio player that allows you to play songs present in your system as well as songs available online, by pasting their URL. There are a lot of other audio players as well that you can use like Audio Pitch & Shift, Boom Audio Player, 3D Audio Player. Passion Audio Player is a simple-to-use tool that allows you to play audio tracks of multiple formats, including OGG, WAV, MP3, APE and WMA. So, you can use basic audio player functions, such as pause and stop, navigate back and forth within the track, as well as adjust the volume. But you can also create, save and manage a playlist, use a search function, view information on each item (e.g.
The audio player needs a moderate amount of CPU and system memory, has a good response time and includes user documentation. One of them applies to audio recognition, which in recent years has been analyzed thoroughly. Logically, similar audio objects should generate similar fingerprints, whereas different files should emanate unlike signatures. One of them is a huge database to search (imagine Youtube's database of video content that is monitored for audio copyright issues). Mathematically speaking a fingermark function F maps the audio object X consisting from a large number of bits to a fingerprint of only a limited number of bits. Nevertheless, there is always a small probability that completely different keys will occupy the same bin (Steve and Mark collision). In next figure, you can visualize the activity diagram, which abstractly describes the logical flow of the fingerprint creation (this flow matches theoretical abstractions described in Content Fingerprinting Using Wavelets, the paper upon which the algorithm is build). Because lots of the components are involved in both activities, they will be described together.
Therefore, the first step (1) in the algorithm is decoding the input file to PCM (Pulse Code Modulation format). If lower sampling rates are used, the original signal's information may not be completely recoverable from the sampled signal. The only thing to mention is that Bass allows you to set the filter for anti-aliasing (very useful for downsampling procedures), while the following code snippets do not take any action against it. In digital signal processing, a spectrogram is an image that shows how the spectral density of a signal varies in time. Building the spectrogram of the image involves the most expensive (from CPU point of view) operation in the entire algorithm - Fast Fourier Transform (O(n*log(n))). Fourier analysis is a family of mathematical techniques, all based on decomposing signals into sinusoids. Generally, this domain can be considered to be one of the most relevant frequency space for Human Auditory System. The 2 most important features of this bit vector are that it is sparse and resistant to small degradations that might appear in the signal. In the case of audio fingerprinting, by applying this kind of transformation on the spectrogram of an audio input, we'll see easily identifiable patterns for consecutive fingerprints.

As you can see, first the log-spectrogram is created, and then the image is sliced into 1.48 sec spectral images (from which the actual fingerprints will be built). Therefore, in the next step (5) of the algorithm we explore the use of Min-Hash to compute sub-fingerprints for these sparse bit vectors. Before exploring how Min-Hash works, I'll point that a very useful characteristic that it has: the probability that MinHash(C1) = MinHash(C2) equals exactly to Jaccard similarity. The method was based on Permutation Grouping: intelligent hash functions for audio and image retrieval paper.
From the initial spectral sub-image (128x32 floats) we've gathered a vector with 100 8-bit integers which represent the fingerprint itself. For instance, 25 hash functions (represented as 4 concatenated min-hashes), and 25 corresponding hash tables, are enough to efficiently partition initial data points into corresponding space address. Now, how can we find similar songs or retrieve information about an unknown audio by having only a small snippet of it?
In order to simplify the explanation, next you can see a generalized image will help you in visualizing them all together. Ideally, the view is defined purely with XAML, with no code-behind other than the standard call to the InitializeComponent method in the constructor.
The audio player also has an equalizer to manually adjust volume settings and supports lots of file formats including MPEG, WAV, MP3, XMPlay-able etc.
You might not get as many features as in VLC Media Player, but it is surely worth giving a try. Winamp support), make the app minimize to the system tray or stay on top of other programs, as well as configure audio settings (e.g.
No error dialogs have popped up in our tests and Passion Audio Player did not freeze or crash. For this reason, in this article you will be introduced to one of complex tasks which arise in the field of computer science: the efficient comparison and recognition of analog signals in digital format. Normally, each song will generate a big amount of fingerprints (in the described algorithm the granularity of each of them is going to be 1.48 sec, so we'll have about 150 objects for 1 song).
Although the perpetual difference of an audio object compressed to MP3 format and the same one compressed to WAVE, is very small, the binary representation of those are totally different, meaning that H(MP3(X)) and H(WAVE(X)) will result in completely dissimilar message digests (hash bins). Following, I will describe in deeper details each activity involved and what component is responsible for it. The PCM format can be considered the raw digital format of an analog signal, in which the magnitude of the signal is sampled regularly at uniform intervals, with each sample being quantized to the nearest value within a range of digital steps. That's why standard Audio-CDs use a sample rate of 44100Hz, as the human ear can typically only recognize frequencies up to 20000Hz.
I should point that, before applying this transformation, each slice of the input signal (371 ms) should be weighted by Hann Window function.
As shown in the following image the discrete Fourier transform changes an N point input signal into two point output signals. Given the time domain signal, the process of calculating the frequency domain is called decomposition, analysis, the forward FFT, or simply, the FFT. All in all, the use of logarithmical spacing in frequency was selected based for simplicity, since the detailed band-edge locations are unlikely to have a strong effect under such coarse sampling (only 32 samples across frequency). Once you get the divided spectral sub-images, they will be further reduced by applying Haar wavelet decomposition on each of them.
Sparsity makes it amendable to further dimensionality reduction through the use of Min Hash. Because the system will work with fingerprints of 8192 bit size, the permutation vector will contain random indexes starting from 0 to 8192. It explores how we can generate random permutations that are more or less identically distributed. Unfortunately, even with this high degree of dimensionality reduction, it is hard to create an efficient system that will have to search within a vector of length 100. Candidate neighbors can be efficiently retrieved by partitioning the probe feature vector in the same way the initial audio object was divided, and collecting the entries in the corresponding hash bins. The overall retrieval process (detection of the query song) is similar to creational one, with an exception that not the entire song is fingerprinted at this time. Having similar wavelet signatures across time, will help us in identifying the song even if there is a small time misalignment between creational and query fingerprints.
First, it will process all the audio files from selected folders, and then will try to detect which of them match to the same signature, thus being duplicates one to each other.
You can also create a separate playlist in the library and see details like artist name, album name.
You can change the appearance of the interface, add new shortcuts, put songs on loop, and much more. Plus, you can adjust the audio balance, load tracks from an audio CD or URL, as well as record sound files. As an example, consider an audio signal ?1, which you would like to compare to another ?2 in order to see if they both are coming from the same song or audio object. In conclusion, there will be a need in using an efficient search algorithm that works well, when the solution is scaled.
Even worst, cryptographic hash functions are usually very sensitive: a single bit of difference in the original object, results in a completely different hash value (thus a small degradation in the key will lead to totally different hash digest).
MP3, OGG, WMA, FLAC) will be first decoded, such that you always will receive PCM samples (here lies the great benefit of using Bass library - it will decode the input file to PCM, with a great support of a lot of modern file formats). Weighting the signal by a window function is done, in order to narrow the domain of a function F to a specified range (usually [-1, 1]). The input signal contains the signal being decomposed, while the two output signals contain the amplitudes of the component sine and cosine waves. If you know the frequency domain, calculation of the time domain is called synthesis, or the inverse FFT. The use of wavelets in audio-retrieval task is done due to their successful use in image retrieval (Fast Multiresolution Image Querying). It's worth mentioning that at this stage (after Top-wavelet extraction), the length of the bit vector we get for 1 fingerprint is 8192 (where only 200 elements are equal to 1, others equal to 0).
Essentially, there is no best answer of how many seconds you should skip between 2 items, as it is mostly detected empirically through testing. It works as follows: think of a column as a set of rows in which the column has a 1 (consider C1 and C2 as 2 fingerprints). For a given permutation, the hash values agree if the first position with a 1 is the same in both bit vectors, and they disagree if the first such position is a row where one but not both, vectors contained a 1. Rather than simply selecting high entropy permutations to place together, a more principled method is to use the Mutual Information (MI) between permutations to guide which permutations are grouped. That's why, in the next step (6) of this article we explore the use of Locality Sensitive Hashing, which is an important class of algorithm for finding nearest neighbors.
The final list of potential neighbors can be created by vote counting, with each hash casting votes for the entries of its indexed bin, and retaining the candidates that receive some minimum number of votes, v.
You may select as many seconds to fingerprint as you want, making a tradeoff between accuracy and processing time. It will be built using WPF framework, and specifically the MVVM pattern, which becomes more popular with the expansion of last. The software not only plays songs in the system but also allows you to play songs available online.
The simplest approach is performing a simple comparison between the query point and each object in the database. The main advantage why hash functions are so widely used in the field of computer science is that they allow comparing 2 large objects X, Y, by just comparing their respective hash values H(X), H(Y).
Particularly, the sampling rate, sample rate, or sampling frequency defines the number of samples per second (or per other unit) taken from a continuous signal to make a discrete signal.

Each frame should be then passed through Fast Fourier transform, in order to get the spectral density varying in time domain.
For instance, a function that is constant inside the interval and zero elsewhere is called a rectangular window, which describes the shape of its graphical representation. The input signal is said to be in the time domain, while the output is in frequency domain. In other words, a wavelet is a wave-like oscillation with amplitude that starts at zero, increases, and then decreases back to zero. In Content Fingerprinting Using Wavelets a static 928 ms stride was used in database creation, and a random 0-46 ms stride was used in querying (random stride was used in order to minimize the coarse effect of unlucky time alignment). Mutual information is a measure of how much knowing the value of one variable reduces the uncertainty in another variable.
It proved to be efficient in the number of comparisons that are required and provides noise-robustness properties. If v = 1, this takes the union of the candidate lists, else if v=L, this takes the intersection.
The Model-View-ViewModel (MVVM) is an architectural pattern used in software engineering that originated from Microsoft as a specialization of the Presentation Model design pattern introduced by Martin Fowler. There are a lot of other features as well, like put songs in loop, create playlist, add shortcuts and more. When you click on the + icon to add a new song to the list, you will see a space to enter the URL. Along with this, there is also one queue list attached with the interface of this audio player.
The difficulty lies in the fact, that each of the signals might have different digitized formats, thus making their binary signatures totally different (resulting in an obsolete byte-by-byte comparison). But, because this method is too time-consuming, the k-nearest neighbor solution (Min-Hash + Locality Sensitive Hashing) will be explored. The equality between later pairs implies the equality between X, Y, with a very low error probability (even though collision cannot be eliminated entirely). This is because the most common type of signal entering the FFT is composed of samples taken at regular intervals of time (in our case 371 ms slices). We can repeat the above procedure multiple times, each time with a new position-permutation. Particularly, the nearest neighbor problem is defined as follows: given a collection of n points, build a data structure which, given any query point, reports the data point that is closest to the query. Once done, the query is sent to the storage to see how many hash tables contain corresponding hash values. The dissimilarity may also result, because of the combination of different internal characteristics of the same audio format (bit rate, sampling rate, number of channels (mono, stereo, etc.)). The inverse of the sampling frequency is the sampling period or sampling interval, which is the time between samples. Wavelets are crafted purposefully to have specific properties that make them useful for signal processing.
If we repeat the process p times, with p different permutations, we get p largely independent projections of the bit vector (e.g. In order to create groups of low mutual information permutations to put together into hashing chunks, the greedy selection procedure was used, that is loosely based on the algorithm used in Approximating Discrete Probability Distributions with Dependence Trees. This problem is of major importance in several areas; some examples are data compression, databases and data mining, information retrieval, image and video databases, machine learning, pattern recognition, statistics and data analysis. If more than v votes are acquired during the query (more than v tables have returned the same fingerprint), the fingerprint is considered a potential match (in Content fingerprinting using wavelets a threshold of 2-5 votes was used).
Even if you proceed with the conversion of the files to some predefined specifications (e.g. The utility that was used in generating the permutations alongside with a lot of other useful features related to this research can be found here (project hosting used in writing the application and associated tools). Typically, the features of each object of interest (document, image, etc.) are represented as a point in Rd and the distance metric is used to measure the similarity of objects. All matches are selected, and analyzed in order to find the best candidate (a simple Hamming distance calculation between the query and potential candidates can be done in order to see what item is the most relevant one). I won't describe the entire permutation grouping algorithm as it is out of the scope of this article. In case of developing a duplicates detector algorithm, the principle of extraction is a bit different.
Once you have added the songs in the library or queue list, you can listen to them one by one and also put them on loop. Starting from 1970's the recording studios tried to push the loudness limit as far as they could, as the motivation was very simple: when comparing two recordings with different levels, listeners are likely to prefer the louder one (see loudness war).
So, turning back to our algorithm, for each of the spectral images, a wavelet signature is computed.
If you are interested, you can research this topic using the material found in this section. So, the aim of this article is to show an efficient algorithm of signal processing which will allow one to have a competent system of sound fingerprinting and signal recognition.
Instead of using the entire set of wavelets, we only keep the ones that most characterize the song (Top 200 is considered a good choice), thus making the signature resistant to noise and other degradations. For example, one can represent a 1000x1000 image as a vector in a 1 000 000-dimensional space, one dimension per pixel. In order to successfully apply the concept of locality sensitive hashing, we need to hash the points using several hash-functions to ensure that for each function the probability of collision is much higher for objects that are close to each other than for those that are far apart (Min-Hash functions adjust to this requirement). Overall, all these parameters are defined empirically, so you might find even better configuration that will work more accurate as the one described here.
Here, I'll try to come with some explanations of the article's algorithm, and also speak about how it can be implemented using C# programming language. Then, one can determine near neighbors by hashing the query point and retrieving elements stored in buckets containing that point. As an example, you may lower the threshold votes, in order to obtain results that will contain also the remixes of the songs matched as duplicates (it's always a trade-off between false-positives and the objective function). Additionally, I'll try to cover topics of digital signal processing that are used in the algorithm, thus you'll be able to get a clearer image of the entire system.
The nearest neighbor problem is an example of an optimization problem: the goal is to find a point which minimizes a certain objective function (in this case, the distance to the query point). As a proof of concept, I'll show you how to develop a simple WPF MVVM application (if you don't know what is MVVM don't go Googling yet, as it is going to be explained further), those solely purpose will be detecting duplicate music files on your local drive, analyzing them by audio content.
To simplify the notation, we say that a point p is an R-near neighbor of a point q if the distance between p and q is at most R.
The easiest way to imagine it is as follows: a classical SQL query on a table will return the exact matches that you supply in the where clause. As opposite, the k-nearest neighbor algorithm will return you not just the exact matches, but also those that are similar to the query parameter.
Instead of returning the exact matches, the algorithm will return you not only the books that contain the initial word in it but also those that contain the words: support, report, airport, deport, portfolio, etc. There are certain applications that can use this feature for solving some specific problems (finding similar text, websites, images, audio items, etc.). In our case it is not just a way of finding similar audio objects, but also a way of increasing the search speed (if the application is scaled, searching through a database that contains vectors of 100 8 bit values, is not a feasible solution).

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