Music discovery is a crucial piece of that puzzle and one that's notoriously challenging to lock into place. In taking its own stab at music recommendation, Google blends technical solutions like machine listening and collaborative filtering with good, old-fashioned human intuition.
Employing both engineers and music editors, the service continually tries to understand what people are listening to, why they enjoy it, and what they might want to hear next. Eck's team is focused on the technical side of this equation, relying on a dual-sided machine learning methodology.
One component of that is collaborative filtering of the variety employed by Netflix and Amazon to recommend horror flicks and toasters. The other involves machine listening. That is, computers "listen" to the audio and try to pick out specific qualities and details within each song.
Collaborative filtering works wonders for the Amazons of the world. But since this type of If-you-like-that-you'll-also-like-this logic works better for kitchen appliances than it does for art, the system needs a way to learn more about the music itself. To teach it, Eck's team leverages Google's robust infrastructure and machine-listening technology to pick apart the granular qualities of each song. These are precisely the kinds of details that Pandora relies on human, professionally trained musicians to figure out. The Internet radio pioneer has long employed musicologists to listen to songs and help build out a multipoint, descriptive data set designed to place each track into a broader context and appropriately relate it to other music.
For Pandora, the results have been extremely valuable, but mapping out this musical intelligence manually doesn't scale infinitely. Thankfully, machine listening has come a long way in recent years. Much like Google indexes the Web, the company is able to index a massive database of audio, mapping the musical qualities found within.
Since it's automated, this part of Google's music recommendation technology can be scaled to a much larger set of data. Indeed, when it comes to music, the tail has never been longer. The world's selection of recorded music was never finite, but today creating and distributing new songs is virtually devoid of friction and financial cost. However much human intelligence as Pandora feeds into its algorithm, its Music Genome Project will never be able to keep up and understand everything.
That's where machine learning gets a leg up. Still, there's a reason Pandora has more than 70 million active listeners and continues to increase its share of overall radio listening time. Its music discovery engine is very good. It might not know about my friend's band on a small Georgia-based record label, but the underlying map of data that Pandora uses to create stations is still incredibly detailed.
When I start a radio station based on Squarepusher, an acclaimed but not particularly popular electronic music artist, the songs it plays are spun for very specific reasons. It plays a track by Aphex Twin because it features "similar electronica roots, funk influences, headnodic beats, the use of chordal patterning, and acoustic drum samples. Pandora knows this much about these tracks thanks to those aforementioned music experts who sat down and taught it. Automated machine listening, by comparison, can't get quite as specific.
At least, not yet. Computers might be able to pick out details about timbre, instruments used, rhythm, and other on-the-surface sonic qualities, but they can only dig so deep. What about when we stretch out and we look what our musical phrase is. That's where the good, old-fashioned human beings come in.
To help flesh out the music discovery and radio experiences in All Access, Google employs music editors who have an intuition that computers have yet to successfully mimic.
Heading up this editor-powered side of the equation is Tim Quirk, a veteran of the online music industry who worked at the now-defunct Listen. Olivier ]. Google's blend of human and machine intelligence is markedly different from Pandora's. Rather than hand-feeding tons of advanced musical knowledge directly into its algorithms, Google mostly keeps the human-curated stuff in its own distinct areas, allowing the computers to do the heavy lifting elsewhere. Quirk and his team of music editors are the ones who define the most important artists, songs and albums in a given genre of which there are hundreds in Google Play Music.
Quirk's team also creates curated playlists and make specific, hand-picked music recommendations.
Computational Music Analysis
To the extent that these manually curated parts of the service influence its users' listening behavior, the human intelligence does find its way back into the algorithms. It just loops back around and takes a longer road to get there. Google's employees aren't the only people feeding intelligence into this semiautomated music machine.
Google is also constantly learning from its users. Like Pandora and its many copycats, Google Play Music's Radio feature has thumbs up and thumbs down buttons, which help inform the way the radio stations work over time. They'll know that John is my favorite, which songs on Revolver I skip, and that of all the heavily Beatles-influenced bands in the world, I love Tame Impala, but loathe Oasis.
The machines will get smarter, much like a toddler does: by watching and listening. As users spend time with the product and tap buttons, the nuanced details will become more obvious. Meanwhile, the ability of computers to actually hear what's contained within each song can only improve over time, just as voice recognition continues to do. The promise of Google Play Music is the same thing that made Google successful to begin with: its ability to use massive data to understand who we are and what we want. If anybody can crack the notoriously hard nut of music discovery in a hugely scalable fashion, it's them.
Just don't expect them to do it with machines alone. We also know what role each artist or song plays in its genre whether they are a key artist for that genre, one of the most commonly played, or an up-and-comer. Most other static genre solutions classify music into rigid, hierarchical relationships, but our system reads everything written about music on the web, and listens to millions of new songs all the time, to identify their acoustic attributes.
To create dynamic genres, The Echo Nest identifies salient terms used to describe music e. We then model genres as dynamic music clusters — groupings of artists and songs that share common descriptors, and similar acoustic and cultural attributes. That means it knows not only what artists and songs fall into a given genre, but also how those songs and artists are trending among actual music fans, within those genres. Our genre system sees these forms of music as they actually exist; it can help the curious music fan hear the differences, for instance, between Luk Thung, Benga, and Zim music.
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Music Analysis by Computer: Ontology and Epistemology
No tag on any scoop yet. Your new post is loading Scooped by Olivier Lartillot. From arstechnica. What makes Beethoven sound like Beethoven? Swiss team used data science to find out. Olivier Lartillot's insight:. From the article:. New state-of-the-art methods in statistics and data science make it possible for us to analyze music in ways that were out of reach for traditional musicology," said co-author Martin Rohrmeier, head of EPFL's Digital Humanities Institute, which is devoted to achieving a better understanding of how music works. Per co-author Markus Neuwirth, "Our approach exemplifies the growing research field of digital humanities, in which data-science methods and digital technologies are used to advance our understanding of real-world sources, such as literary texts, music, or paintings under new digital perspectives.
For their study, the Swiss team focused on the composer's 16 string quartets—over eight hours of music in total, with 70 individual movements. From there, they built up a dataset based on nearly 30, chord annotations made by music theorists. Then they applied a variety of statistical techniques to hunt for patterns within that dataset. The team focused its efforts on looking for structural regularities in the dataset, noting that tonal harmony is the most central concept when it comes to Western music, most dominant from the midth through the late 19th century.
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But the researchers note that previous theoretical approaches to this subject "suffer from a lack of empirical foundation," relying more on qualitative descriptions of just a few examples. In contrast, they believe they can offer a more "quantifiable and testable hypothesis. So what makes Beethoven sound like Beethoven? The Swiss researchers relied on a harmonic perspective to decode the maestro's distinctive compositional style. Dominant and tonic chords aka V and I chords are the most common in classical music, playing a vital role in musical phraseology, and there are many variants within those two broad classifications.
According to the EPFL team, Beethoven's string quarters contain more than 1, different types of chords. And we found, importantly, that the order in which the chords appear matters a lot, so you cannot play the music backwards. Furthermore, by studying the distribution of chords Beethoven used in his string quartets—how often each occurred, for instance, and how often one transitioned to another—they were able to define a statistical signature for the composer. In the future, the Swiss team hopes to expand the application of their statistical techniques to other 19th-century composers, such as Frederic Chopin or Franz Liszt, and perhaps add other musical dimensions into the mix, like rhythm, meter, and instrumentation.
No comment yet. Google's engineers say that lack of rigor is ruining AI research —. From qz. The authors suggest a range of fixes, all focused on learning which algorithms work best, when, and why. From www. The mathematics of Joseph Fourier, born years ago this week, shows the value of intellectual boldness. When you listen to digital music, the harmonies and chords that you hear have probably been reconstructed from a file that stored them as components of different frequencies, broken down by a process known as Fourier analysis. As you listen, the cochleae in your ears repeat the process — separating the sounds into those same sinusoidal components before sending electrical signals to the brain, which puts the components together again.
Fourier analysis allows complex waveforms to be understood and analysed by breaking them down into simpler signals. The roots of the idea go back to the mids, when the Italian mathematical physicist Joseph-Louis Lagrange and others studied the vibration of strings and the propagation of sound. Fourier was born years ago this week, on 21 March Today, there is virtually no branch of science, technology and engineering that is left untouched by his ideas. Among the scientists who benefited is Ingrid Daubechies, an applied mathematician, who in the s helped to develop the theory of wavelets, which generalized Fourier analysis and opened up previously inaccessible problems.
Fourier wanted to understand how heat propagates in a solid object. He discovered the equation that governs this, and showed how to solve it — predicting how the temperature distribution will evolve, starting from the known distribution at an initial time. To do so, he broke the temperature profile down into trigonometric functions, as if it were a sound wave.
This possibility horrified mathematicians at the time, who were much more comfortable with smooth curves that promised aesthetic simplicity. Fourier stuck to his guns and, as he developed his ideas, started to win his critics over. The Artificial Intelligence Revolution: The exponential rise of Superintelligence, and its consequences.. From waitbutwhy. Part 1 of 2: "The Road to Superintelligence". Artificial Intelligence — the topic everyone in the world should be talking about. Computer-automated music analysis and music composition is a particular application of Artificial Intelligence AI.
In my view, a good music analysis, even of a particular aspect of music, requires taking into consideration a large range of musical considerations, because in music everything is intertwined. Hence a good computer automation of music analysis would need to achieve a certain degree of Artificial General Music Intelligence not necessarily all aspects of human intelligence, but all that is required to fully understand music. But the main point of the linked article is about the third AI Caliber, Artificial Superintelligence ASI , where the machine develops a degree of intelligence that is far superior to our human capabilities.
The application to music analysis and composition would be extremely exciting. Once the machine would be able to reach the same degree of music intelligence than, say, Bach or Boulez — and to flood us with an infinite amount of extremely refined music, it would immediately completely transcend our mere human capabilities and start developing a kind of Supermusic, somewhat unfathomable to us.
It would not be of much interest if we cannot grasp anything about it, but if the machine can still try to let us find ways to approach this artistic monster, by keeping cognitive constraints in the composition and by offering some kind of guiding auditory tools, this would be fantastic. Well, the linked article is not about music but about the general characteristics of ASI as well as the terrible underlying risks of existential threat. Kurzweil suggests that the progress of the entire 20th century would have been achieved in only 20 years at the rate of advancement in the year —in other words, by , the rate of progress was five times faster than the average rate of progress during the 20th century.
All in all, because of the Law of Accelerating Returns, Kurzweil believes that the 21st century will achieve 1, times the progress of the 20th century. There are three major AI caliber categories:. For instance, your phone is a little ANI factory. Nothing will make you appreciate human intelligence like learning about how unbelievably challenging it is to try to create a computer as smart as we are. Building skyscrapers, putting humans in space, figuring out the details of how the Big Bang went down—all far easier than understanding our own brain or how to make something as cool as it.
As of now, the human brain is the most complex object in the known universe. Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Make AI that can beat any human in chess? Google is currently spending billions of dollars trying to do it. Hard things—like calculus, financial market strategy, and language translation—are mind-numbingly easy for a computer, while easy things—like vision, motion, movement, and perception—are insanely hard for it.
Those things that seem easy to us are actually unbelievably complicated, and they only seem easy because those skills have been optimized in us and most animals by hundreds of millions of years of animal evolution. When you reach your hand up toward an object, the muscles, tendons, and bones in your shoulder, elbow, and wrist instantly perform a long series of physics operations, in conjunction with your eyes, to allow you to move your hand in a straight line through three dimensions.
It seems effortless to you because you have perfected software in your brain for doing it. And everything we just mentioned is still only taking in stagnant information and processing it. To be human-level intelligent, a computer would have to understand things like the difference between subtle facial expressions, the distinction between being pleased, relieved, content, satisfied, and glad, and why Braveheart was great but The Patriot was terrible.
But there are a bunch of far-fetched strategies out there and at some point, one of them will work. Here are the three most common strategies I came across:. The science world is working hard on reverse engineering the brain to figure out how evolution made such a rad thing—optimistic estimates say we can do this by If the brain is just too complex for us to emulate, we could try to emulate evolution instead.
This is when scientists get desperate and try to program the test to take itself. But it might be the most promising method we have. And that would be their main job—figuring out how to make themselves smarter. Rapid advancements in hardware and innovative experimentation with software are happening simultaneously, and AGI could creep up on us quickly and unexpectedly for two main reasons:. Or, when it comes to something like a computer that improves itself, we might seem far away but actually be just one tweak of the system away from having it become 1, times more effective and zooming upward to human-level intelligence.
AGI with an identical level of intelligence and computational capacity as a human would still have significant advantages over humans. These leaps make it much smarter than any human, allowing it to make even bigger leaps. As the leaps grow larger and happen more rapidly, the AGI soars upwards in intelligence and soon reaches the superintelligent level of an ASI system.
There is some debate about how soon AI will reach human-level general intelligence. Superintelligence of that magnitude is not something we can remotely grasp. What it would mean for a machine to be superintelligent? A key distinction is the difference between speed superintelligence and quality superintelligence. A large portion of the people who know the most about this topic would agree that is a very reasonable estimate for the arrival of potentially world-altering ASI. Only 45 years from now. And in the case of a fast takeoff, if it achieved ASI even just a few days before second place, it would be far enough ahead in intelligence to effectively and permanently suppress all competitors.
The singleton phenomenon can work in our favor or lead to our destruction. If the people thinking hardest about AI theory and human safety can come up with a fail-safe way to bring about Friendly ASI before any AI reaches human-level intelligence, the first ASI may turn out friendly. It could then use its decisive strategic advantage to secure singleton status and easily keep an eye on any potential Unfriendly AI being developed. This may be the most important race in human history.
Recommendation systems are seriously harming the "biodiversity" of music. Technologies such as recommendation systems are seriously harming the "biodiversity" of music. This article raises a very important issue that should be very carefully be taken into consideration by the music industry and especially by the people designing the new technologies: namely, that technologies such as recommendation systems are seriously harming the "biodiversity" of music.
I think the Music Information Retrieval community, in particular, should take the time to become aware of this important problem, and should develop strategies, including axes of research, to counteract these highly worrying developments. Maybe research have already been developed along those lines, in such case it would be important to learn more about them. That reason is that, with a very few exceptions, no one cares any more.
Much has been made of the transition from an analogue to a binary age. Not so much has been made of the even more insidious transition from a binary to an algorithmic age. There is a limited understanding of the algorithms used by Google, Amazon, Facebook and other social media platforms to create content filter bubbles which ensure that we stay in our self-defined comfort zones.
Even less attention has been paid to how the algorithms virus has expanded beyond online platforms. For example the Guardian uses editorial algorithms to unashamedly slant its journalism towards the prejudices of its readership, and concert promoters use subjective algorithms to present concerts of familiar and non-challenging repertoire. The problem is that no one cares that this is happening. In fact everyone feels very contended in their own comfort zone with ever faster broadband, ever cheaper streamed content, ever more friends and followers, ever more selfie opportunities and - most importantly - ever fewer challenges to their prejudices.
And the media - particularly the classical music media - is quite happy to play along; because keeping your readers in their comfort zone means keeping your readers. Today the vast majority no longer care about protecting the arts. And we are all to blame. This article is being written on a free blogging platform provided by Google, the pioneer of algorithmic determination.
If it reaches any audience at all it will be because it is favoured by the algorithms of Facebook and Twitter. However, it is unlikely to reach any significant social media audience because my views are not favoured by the online vigilantes who police the borders of classical music's comfort zones.
And for the same reason the dissenting views expressed here and elsewhere are unlikely to find their way into the Guardian or Spectator or to be aired on BBC Radio 3's Music Matters. But why should any of this matter? Why should people care when they can watch safe within the comfort zone of their own home an outstanding performance lasting 2 hours 44 minutes of Berlioz's Damnation of Faust by the world-class forces of Simon Rattle and the London Symphony Orchestra recorded in high quality video and audio for free on YouTube?
There is no viable solution because we are all part of the problem. Classical music's biggest challenge is not ageing audiences, disruptive business models, institutionalised discrimination, unsatisfactory concert halls etc etc. The biggest challenge facing classical music is adapting to a society in which no one cares about anything except staying firmly within their own algorithmically defined comfort zone.
From appleinsider. In an escalating war with Apple Music, streaming music market leader Spotify on Thursday announced the acquisition of Niland, a small machine learning startup whose technology will help deliver song recommendations to users. A small startup headquartered in Paris, Niland developed machine learning algorithms to analyze digital music. Prior to Spotify's purchase, Niland offered music search and recommendation services to "innovative music companies" through custom APIs. For example, Niland marketed its AI and audio processing technology by offering content creators and publishers a specialized audio search engine.
Customers were able to upload tracks for processing and receive a list of similar sounding songs. The technology could also be used to surface similar tracks within a particular catalog, making for a powerful recommendation engine.
Going further, Niland's tech can extract complex metadata pertaining to mood, type of voice, instrumentation, genre and tempo. The firm's APIs automatically process and tag these sonic watermarks for keyword searches like "pop female voice" or "jazz saxophone. Some of the same features went into a music recommendation engine that offered suggestions based on mood, activity, genre, song style and other factors.
Spotify is most likely looking to integrate the AI-based engine into its app in the near term. Niland's team will be relocated to Spotify's office in New York, where they will help the streaming giant improve its recommendation and personalization technologies. The move comes amidst a wider race to deliver the perfect personalized listening experience. Industry rivals are looking for ways to develop customized playlists, and Spotify appears to be investing heavily in intelligent software. Apple Music, on the other hand, touted human-curated playlists when it launched in News of the Niland acquisition arrives less than a month after Spotify announced the purchase of Mediachain Labs, a blockchain startup focused that developed technologies for registering, identifying, tracking and managing content across the internet.
The buying spree comes amid rapid growth for Spotify, which in March hit a milestone 50 million paid subscribers.
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Counting free-to-stream listeners, the service is said to boast more than million users. By comparison, Apple Music reached 20 million in December, though Apple executive Jimmy Iovine in a recent interview said the product would have " million people on it" if a free tier was offered.
From fr. My quick translation:. A pioneer in computer music, first working in US, J. He was, through his double training, scientific and artistic, the first French composer to open the way to sounds synthesized by computer. He is now a major figure in contemporary music creation and, at the same time, electronic music research.
His contributions mark the aesthetics of the years He obtained a first prize in the UFAM piano competition in His PhD thesis focuses on the analysis, synthesis and perception of musical sounds. His work accounts for the complexity and diversity of the mechanisms involved in hearing. He perceived the limit and inadequacies of models prevailing at that time.
His approach focused on timbre has the merit to illuminate the preoccupations now central to music computing: to unite two fields of knowledge physics of sound and music , and to exploit with this design a new promising technology, namely computer. The artistic direction of the "Computer" department allowed him to study the integration of computer science in music research.
A musician and composer recognized by the international artistic community, Jean-Claude Risset is also a technician and an undisputed theoretician of computer music. Spotify is launching a new playlist service called Discover Weekly that uses your data to serve you songs you might like. From medium. Finally, they plotted the popularity of each style over time.
This might change as the company moves away from being an open source platform, useful to Via niland. From venturebeat. Deep learning amounts to one of those technologies that several companies could start to implement in the future, in order to improve music streaming. Computer becomes a bird enthusiast. From news. Program can distinguish among hundreds of species in recorded birdsongs.
A branch of A. But meaning, reasoning and common sense remain elusive. Could have an important impact in computational music analysis. For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing vast amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach.
Companies like Google, Facebook and Microsoft have poured money into deep learning. Start-ups pursuing everything from cancer cures to back-office automation trumpet their deep learning expertise. In recent conversations, online comments and a few lengthy essays, a growing number of A. And I think that trusting these brute force algorithms too much is a faith misplaced. Although the technique has spawned successes, the results are largely confined to fields where those huge data sets are available and the tasks are well defined, like labeling images or translating speech to text.
The technology struggles in the more open terrains of intelligence — that is, meaning, reasoning and common-sense knowledge. Researchers have shown that deep learning can be easily fooled. Scramble a relative handful of pixels, and the technology can mistake a turtle for a rifle or a parking sign for a refrigerator. As is so often the case, the patterns extracted by deep learning are more superficial than they initially appear.
If the reach of deep learning is limited, too much money and too many fine minds may now be devoted to it. While that program and other efforts vary, their common goal is a broader and more flexible intelligence than deep learning. And they are typically far less data hungry. They often use deep learning as one ingredient among others in their recipe. Those other, non-deep learning tools are often old techniques employed in new ways. At Kyndi, a Silicon Valley start-up, computer scientists are writing code in Prolog, a programming language that dates to the s.
It was designed for the reasoning and knowledge representation side of A.
Deep learning comes from the statistical side of A. Kyndi has been able to use very little training data to automate the generation of facts, concepts and inferences. The Kyndi system can train on 10 to 30 scientific documents of 10 to 50 pages each. Kyndi and others are betting that the time is finally right to take on some of the more daunting challenges in A. That echoes the trajectory of deep learning, which made little progress for decades before the recent explosion of digital data and ever-faster computers fueled leaps in performance of its so-called neural networks. Those networks are digital layers loosely analogous to biological neurons.
There are other hopeful signs in the beyond-deep-learning camp.
Vicarious, a start-up developing robots that can quickly switch from task to task like humans, published promising research in the journal Science last fall. Its A. We discuss two sets of problems, segmentation and form analysis. We present studies in music information retrieval MIR related to both problems.
Thinking about codification and automatic analysis of musical forms will help the development of better MIR algorithms. Keywords : computational music analysis music information retrieval form analysis music structure. Document type : Book sections. Computational Analysis of Musical Form. David Meredith. Computational Music Analysis , Springer, pp. Metrics Record views.