How Meta’s AI Generates Music Based mostly on a Reference Melody | by Max Hilsdorf | Jun, 2023


MusicGen, analyzed

Picture by writer.

On June thirteenth, 2023, Meta (previously Fb) made waves within the music and AI communities with the discharge of their generative music mannequin, MusicGen. This mannequin not solely surpasses Google’s MusicLM, which was launched earlier this yr, by way of capabilities however can be skilled on licensed music information and open-sourced for non-commercial use.

This implies which you could not solely learn the research paper or hearken to demos but additionally copy their code from GitHub or experiment with the mannequin in an internet app on HuggingFace.

Along with producing audio from a textual content immediate, MusicGen may generate music primarily based on a given reference melody, a characteristic often called melody conditioning. On this weblog submit, I’ll reveal how Meta applied this convenient and engaging performance into their mannequin. However earlier than we delve into that, let’s first perceive how melody conditioning works in observe.

Base Observe

The next is a brief digital music snippet that I produced for this text. It options digital drums, two dominant 808 bass and two syncopated synths. When listening to it, attempt to determine the “predominant melody” of the monitor.

Utilizing MusicGen, I can now generate music in different genres that stick with the identical predominant melody. All I would like for that’s my base monitor and a textual content immediate describing how the brand new piece ought to sound.

Orchestral Variant

A grand orchestral association with thunderous percussion, epic brass fanfares, and hovering strings, making a cinematic environment match for a heroic battle.

Reggae Variant

basic reggae monitor with an digital guitar solo

Jazz Variant

clean jazz, with a saxophone solo, piano chords, and snare full drums

How Good are the Outcomes?

Though MusicGen doesn’t adhere carefully to my textual content prompts and creates music that’s barely completely different from what I requested for, the generated items nonetheless precisely replicate the requested style and, extra importantly, each bit showcases its personal interpretation of the primary melody from the bottom monitor.

Whereas the outcomes are usually not excellent, I discover the capabilities of this mannequin to be fairly spectacular. The truth that MusicGen has been one of the crucial common fashions on HuggingFace ever since its launch additional emphasizes its significance. With that mentioned, let’s delve deeper into the technical points of how melody conditioning works.

Three text-music pairs as they’re used for coaching fashions like MusicLM or MusicGen. Picture by writer.

Virtually all present generative music fashions observe the identical process throughout coaching. They’re supplied with a big database of music tracks accompanied by corresponding textual content descriptions. The mannequin learns the connection between phrases and sounds, in addition to the best way to convert a given textual content immediate right into a coherent and gratifying piece of music. Through the coaching course of, the mannequin optimizes its personal compositions by evaluating them to the actual music tracks within the dataset. This permits the mannequin to determine its strengths and areas that require enchancment.

The difficulty lies in the truth that as soon as a machine studying mannequin is skilled for a selected activity, akin to text-to-music era, it’s restricted to that individual activity. Whereas it’s potential to make MusicGen carry out sure duties that it was not explicitly skilled for, like persevering with a given piece of music, it can’t be anticipated to sort out each music era request. As an illustration, it can’t merely take a melody and rework it into a special style. This might be like throwing potatoes right into a toaster and anticipating fries to return out. As an alternative, a separate mannequin have to be skilled to implement this performance.

Let’s discover how Meta tailored the mannequin coaching process to allow MusicGen to generate variations of a given melody primarily based on a textual content immediate. Nonetheless, there are a number of challenges related to this strategy. One of many major obstacles is the anomaly in figuring out “the melody” of a music and representing it in a computationally significant means. Nonetheless, for the aim of understanding the brand new coaching process at a broader stage, let’s assume a consensus on what constitutes “the melody” and the way it may be simply extracted and fed into the mannequin. On this situation, the adjusted coaching methodology may be outlined as follows:

Three text-music-melody pairs as they have been used for instructing MusicGen melody-conditioned era.

For every monitor within the database, step one is to extract its melody. Subsequently, the mannequin is fed with each the monitor’s textual content description and its corresponding melody, prompting the mannequin to recreate the unique monitor. Basically, this strategy simplifies the unique coaching goal, the place the mannequin was solely tasked with recreating the monitor primarily based on textual content.

To know why we do that, let’s ask ourselves what the AI mannequin learns on this coaching process. In essence, it learns how a melody may be changed into a full piece of music primarily based on a textual content description. Which means after the coaching, we are able to present the mannequin with a melody and request it to compose a chunk of music with any style, temper, or instrumentation. To the mannequin, this is similar “semi-blind” era activity it has efficiently completed numerous instances throughout coaching.

Having grasped the method employed by Meta to show the mannequin melody-conditioned music era, we nonetheless must sort out the problem of exactly defining what constitutes “the melody.”

The reality is, there isn’t any goal methodology to find out or extract “the melody” of a polyphonic musical piece, besides when all devices are enjoying in unison. Whereas there’s usually a outstanding instrument akin to a voice, guitar, or violin, it doesn’t essentially indicate that the opposite devices are usually not a part of “the melody.” Take Queen’s “Bohemian Rhapsody” for example. If you consider the music, you would possibly first recall Freddie Mercury’s predominant vocal melodies. Nonetheless, does that imply the piano within the intro, the background singers within the center part, and the electrical guitar earlier than “So that you suppose you possibly can stone me […]” are usually not a part of the melody?

One methodology for extracting “the melody” of a music is to think about essentially the most outstanding melody as essentially the most dominant one, usually recognized because the loudest melody within the combine. The chromagram is a broadly utilized illustration that visually shows essentially the most dominant musical notes all through a monitor. Beneath, you will discover the chromagram of the reference monitor, initially with the entire instrumentation after which excluding drums and bass. On the left aspect, essentially the most related notes for the melody (B, F#, G) are highlighted in blue.

Each chromagrams precisely depict the first melody notes, with the model of the monitor with out drums and bass offering a clearer visualization of the melody. Meta’s research additionally revealed the identical remark, which led them to make the most of their supply separation software (DEMUCS) to take away any disturbing rhythmic components from the monitor. This course of leads to a sufficiently consultant rendition of “the melody,” which may then be fed to the mannequin.

In abstract, we are able to now join the items to know the underlying course of when requesting MusicGen to carry out melody-conditioned era. Here’s a visible illustration of the workflow:

How MusicGen produces a melody-conditioned music output. Picture by writer.
Picture by Xavier von Erlach on Unsplash

Whereas MusicGen exhibits promising developments in melody-conditioning, you will need to acknowledge that the expertise remains to be a work-in-progress. Chromagrams, even when drums and bass are eliminated, supply an imperfect illustration of a monitor’s melody. One limitation is that chromagrams categorize all notes into the 12 western pitch courses, which means they seize the transition between two pitch courses however not the route (up or down) of the melody.

As an illustration, the melodic interval between transferring from C4 to G4 (an ideal fifth) differs considerably from transferring from C4 to G3 (an ideal fourth). Nonetheless, in a chromagram, each intervals would seem the identical. The difficulty worsens with octave jumps, because the chromagram would point out the melody stayed on the identical be aware. Think about how a chromagram would misread the emotional octave soar carried out by Céline Dion in “My Coronary heart Will Go On” through the line “wher-e-ver you might be” as a secure melodic motion. To reveal this, simply take a look at the chromagram for the refrain in A-ha’s “Tackle Me”, under. Does this replicate your concept of the music’s melody?

A chromagram of the refrain in “Tackle Me” (A-ha), bass and drums eliminated. Picture by writer.

One other problem is the inherent bias of the chromagram. It performs properly in capturing the melody of some songs whereas utterly lacking the mark in others. This bias is systematic relatively than random. Songs with dominant melodies, minimal interval jumps, and unison enjoying are higher represented by the chromagram in comparison with songs with advanced melodies unfold throughout a number of devices and that includes giant interval jumps.

Moreover, the restrictions of the generative AI mannequin itself are value noting. The output audio nonetheless reveals noticeable variations from human-made music, and sustaining a constant fashion over a six-second interval stays a battle. Furthermore, MusicGen falls quick in faithfully capturing the extra intricate points of the textual content immediate, as evidenced by the examples offered earlier. It would require additional technological developments for melody-conditioned era to succeed in a stage the place it may be used not just for amusement and inspiration but additionally for producing end-user-friendly music.

Picture by Marc Sendra Martorell on Unsplash

How can we enhance the AI?

From my perspective, one of many major issues that future analysis ought to handle relating to melody-conditioned music era is the extraction and illustration of “the melody” from a monitor. Whereas the chromagram is a well-established and simple sign processing methodology, there are quite a few newer and experimental approaches that make the most of deep studying for this goal. It could be thrilling to witness corporations like Meta drawing inspiration from these developments, lots of that are coated in a complete 72-page overview by Reddy et al. (2022).

Concerning the standard of the mannequin itself, each the audio high quality and the comprehension of textual content inputs may be enhanced by means of scaling up the scale of the mannequin and coaching information, in addition to the event of extra environment friendly algorithms for this particular activity. For my part, the discharge of MusicLM in January 2023 resembles a “GPT-2 second.” We’re starting to witness the capabilities of those fashions, however important enhancements are nonetheless wanted throughout numerous points. If this analogy holds true, we are able to anticipate the discharge of a music era mannequin akin to GPT-3 prior to we would anticipate.

How does this impression musicians?

As is commonly the case with generative music AI, issues come up relating to the potential damaging impression on the work and livelihoods of music creators. I anticipate that sooner or later, it is going to develop into more and more difficult to earn a residing by creating variations of present melodies. That is significantly evident in situations akin to jingle manufacturing, the place corporations can effortlessly generate quite a few variations of a attribute jingle melody at minimal price for brand new advert campaigns or personalised ads. Undoubtedly, this poses a menace to musicians who depend on such actions as a big supply of earnings. I reiterate my plea for creatives concerned in producing music valued for its goal musical qualities relatively than subjective, human qualities (akin to inventory music or jingles) to discover various earnings sources to organize for the long run.

On the optimistic aspect, melody-conditioned music era presents an unimaginable software for enhancing human creativity. If somebody develops a fascinating and memorable melody, they will rapidly generate examples of the way it would possibly sound in numerous genres. This course of may also help determine the perfect style and elegance to carry the music to life. Furthermore, it provides a chance to revisit previous tasks inside one’s music catalogue, exploring their potential when translated into completely different genres or types. Lastly, this expertise lowers the entry barrier for creatively inclined people with out formal musical coaching to enter the sphere. Anybody can now provide you with a melody, hum it right into a smartphone microphone, and share outstanding preparations of their concepts with associates, household, and even try to succeed in a wider viewers.

The query of whether or not AI music era is helpful to our societies stays open for debate. Nonetheless, I firmly imagine that melody-conditioned music era is among the use instances of this expertise that genuinely enhances the work of each skilled and aspiring creatives. It provides worth by providing new avenues for exploration. I’m eagerly wanting ahead to witnessing additional developments on this area within the close to future.


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