Now we are watching Frozen 2 on Disney+ and are experiencing it once again on a different TV / sound system! It's really only noticable during songs. It feels "flat" overall and the voices get lost in the music.
It seems like Disney+ has compressed the mixes of movies, possibly to avoid annoying dynamics in movie audio, but it has a poor impact on songs. Anyone experiencing something similar or have any fixes?
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If your slide show is longer than one song, you can add more songs. However, if you find that you're having trouble synchronizing the music with the slide show, you can use a third-party audio editing tool, such as Audacity, to string the songs together into one file so they play continuously throughout the slide show.
The goal of mixing is to bring out the best in your multi-track recording by adjusting levels, panning, and audio effects for each individual track. The aim is to sculpt your arrangement to make sense of all your sounds in relation to each other.
If you're a music production beginner, all of these file abbreviations can become confusing. Everything gets a bit simpler though when you realise that all audio formats can be split into three major categories:
The popularity of MP3 files stems from their manageable file size and the marginal loss of sound data that only exists beyond the hearing range of normal people. These file types are able to reduce the quality of sounds that aren't easy to hear and compress all other audio data as efficiently as possible.
Files take up very little space and are great for streaming, especially over mobile devices. Requiring less than 1 MB per minute of music and sounding better than MP3 at the same bitrate, AAC files are used by iTunes, Apple Music and Android, as well as games brands Nintendo and Sony PlayStation.
Many music professionals consider OGG to be a more efficient alternative to MP3, with the format providing better sound at the same bitrate - giving the same audio quality through a smaller file size.
Opposite lossy compression comes lossless compression. This method reduces an audio file's size without sacrificing any loss of data between the source audio file and the compressed audio file. Sounds great, right?
PCM is the most common uncompressed audio format used in CDs and DVDs. Standing for Pulse-Code Modulation, PCM offers a digital recording of raw analog audio signals. Analog sounds exist in the form of waveforms, so sound must be recorded at specific intervals (also known as pulses) in order to be converted into digital bits.
When storage was the most important factor, MP3s became the go-to file format. Nowadays, our phones and laptops have far more available storage space so you have the option of choosing higher-quality file formats to get the best possible sound out of your songs.
Automatic music generation dates back to more than half a century.[^reference-1][^reference-2][^reference-3][^reference-4] A prominent approach is to generate music symbolically in the form of a piano roll, which specifies the timing, pitch, velocity, and instrument of each note to be played. This has led to impressive results like producing Bach chorals,[^reference-5][^reference-6] polyphonic music with multiple instruments,[^reference-7][^reference-8][^reference-9] as well as minute long musical pieces.[^reference-10][^reference-11][^reference-12]
One way of addressing the long input problem is to use an autoencoder that compresses raw audio to a lower-dimensional space by discarding some of the perceptually irrelevant bits of information. We can then train a model to generate audio in this compressed space, and upsample back to the raw audio space.[^reference-25][^reference-17]
We chose to work on music because we want to continue to push the boundaries of generative models. Our previous work on MuseNet explored synthesizing music based on large amounts of MIDI data. Now in raw audio, our models must learn to tackle high diversity as well as very long range structure, and the raw audio domain is particularly unforgiving of errors in short, medium, or long term timing.
We use three levels in our VQ-VAE, shown below, which compress the 44kHz raw audio by 8x, 32x, and 128x, respectively, with a codebook size of 2048 for each level. This downsampling loses much of the audio detail, and sounds noticeably noisy as we go further down the levels. However, it retains essential information about the pitch, timbre, and volume of the audio.
Next, we train the prior models whose goal is to learn the distribution of music codes encoded by VQ-VAE and to generate music in this compressed discrete space. Like the VQ-VAE, we have three levels of priors: a top-level prior that generates the most compressed codes, and two upsampling priors that generate less compressed codes conditioned on above.
The top-level prior models the long-range structure of music, and samples decoded from this level have lower audio quality but capture high-level semantics like singing and melodies. The middle and bottom upsampling priors add local musical structures like timbre, significantly improving the audio quality.
We train these as autoregressive models using a simplified variant of Sparse Transformers.[^reference-29][^reference-30] Each of these models has 72 layers of factorized self-attention on a context of 8192 codes, which corresponds to approximately 24 seconds, 6 seconds, and 1.5 seconds of raw audio at the top, middle and bottom levels, respectively.
Once all of the priors are trained, we can generate codes from the top level, upsample them using the upsamplers, and decode them back to the raw audio space using the VQ-VAE decoder to sample novel songs.
To train this model, we crawled the web to curate a new dataset of 1.2 million songs (600,000 of which are in English), paired with the corresponding lyrics and metadata from LyricWiki. The metadata includes artist, album genre, and year of the songs, along with common moods or playlist keywords associated with each song. We train on 32-bit, 44.1 kHz raw audio, and perform data augmentation by randomly downmixing the right and left channels to produce mono audio.
The top-level transformer is trained on the task of predicting compressed audio tokens. We can provide additional information, such as the artist and genre for each song. This has two advantages: first, it reduces the entropy of the audio prediction, so the model is able to achieve better quality in any particular style; second, at generation time, we are able to steer the model to generate in a style of our choosing.
To match audio portions to their corresponding lyrics, we begin with a simple heuristic that aligns the characters of the lyrics to linearly span the duration of each song, and pass a fixed-size window of characters centered around the current segment during training. While this simple strategy of linear alignment worked surprisingly well, we found that it fails for certain genres with fast lyrics, such as hip hop. To address this, we use Spleeter[^reference-32] to extract vocals from each song and run NUS AutoLyricsAlign[^reference-33] on the extracted vocals to obtain precise word-level alignments of the lyrics. We chose a large enough window so that the actual lyrics have a high probability of being inside the window.
To attend to the lyrics, we add an encoder to produce a representation for the lyrics, and add attention layers that use queries from the music decoder to attend to keys and values from the lyrics encoder. After training, the model learns a more precise alignment.
While Jukebox represents a step forward in musical quality, coherence, length of audio sample, and ability to condition on artist, genre, and lyrics, there is a significant gap between these generations and human-created music.
For example, while the generated songs show local musical coherence, follow traditional chord patterns, and can even feature impressive solos, we do not hear familiar larger musical structures such as choruses that repeat. Our downsampling and upsampling process introduces discernable noise. Improving the VQ-VAE so its codes capture more musical information would help reduce this. Our models are also slow to sample from, because of the autoregressive nature of sampling. It takes approximately 9 hours to fully render one minute of audio through our models, and thus they cannot yet be used in interactive applications. Using techniques[^reference-27][^reference-34] that distill the model into a parallel sampler can significantly speed up the sampling speed. Finally, we currently train on English lyrics and mostly Western music, but in the future we hope to include songs from other languages and parts of the world.
We collect a larger and more diverse dataset of songs, with labels for genres and artists. Model picks up artist and genre styles more consistently with diversity, and at convergence can also produce full-length songs with long-range coherence.
We scale our VQ-VAE from 22 to 44kHz to achieve higher quality audio. We also scale top-level prior from 1B to 5B to capture the increased information. We see better musical quality, clear singing, and long-range coherence. We also make novel completions of real songs.
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