Some prioritize the availability of an extensive library of songs and albums, while others prioritize user interface and user experience. On the other hand, some lesser-known services have logos that may not be immediately recognizable but are an integral part of a larger and evolving ecosystem.ĭifferent platforms have their own advantages and favorable sides. Spotify’s green icon and Apple Music’s musical notes are well-recognized brands in the industry. In terms of branding, some logos have become iconic symbols. For example, some specialize in high sound quality, while others specialize in genres or regions underrepresented on major platforms. Many lesser-known services are noteworthy, not least because they offer unique features or target specific musical tastes. The field is not limited to just a few dominant platforms. Other mainstream platforms include music streaming service YouTube and Amazon’s offering, both of which have significant user bases. They offer a wide range of songs, podcasts, and other audio entertainment. Major services such as Spotify and Apple Music have established a significant presence in this sector and have become household names. This medium has grown significantly over the past decade, leading to the emergence of different platforms and recognizable logos. Specifically, PopMAG wins 42\%/38\%/40\% votes when comparing with ground truth musical pieces on LMD, FreeMidi and CPMD datasets respectively and largely outperforms other state-of-the-art music accompaniment generation models and multi-track MIDI representations in terms of subjective and objective metrics.Music streaming services have dramatically changed the way we consume and interact with music. The results demonstrate the effectiveness of PopMAG for multi-track harmony modeling and long-term context modeling. We evaluate PopMAG on multiple datasets (LMD, FreeMidi and CPMD, a private dataset of Chinese pop songs) with both subjective and objective metrics. We call our system for pop music accompaniment generation as PopMAG. 2) We introduce extra long-context as memory to capture long-term dependency in music. We further introduce two new techniques to address this challenge: 1) We model multiple note attributes (e.g., pitch, duration, velocity) of a musical note in one step instead of multiple steps, which can shorten the length of a MuMIDI sequence. While this greatly improves harmony, unfortunately, it enlarges the sequence length and brings the new challenge of long-term music modeling. To improve harmony, in this paper, we propose a novel MUlti-track MIDI representation (MuMIDI), which enables simultaneous multi-track generation in a single sequence and explicitly models the dependency of the notes from different tracks. Previous works usually generate multiple tracks separately and the music notes from different tracks not explicitly depend on each other, which hurts the harmony modeling. Download a PDF of the paper titled PopMAG: Pop Music Accompaniment Generation, by Yi Ren and 5 other authors Download PDF Abstract:In pop music, accompaniments are usually played by multiple instruments (tracks) such as drum, bass, string and guitar, and can make a song more expressive and contagious by arranging together with its melody.
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