REVIEW 10 cited by
Mel-Band RoFormer for Music Source Separation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recently, multi-band spectrogram-based approaches such as Band-Split RNN (BSRNN) have demonstrated promising results for music source separation. In our recent work, we introduce the BS-RoFormer model which inherits the idea of band-split scheme in BSRNN at the front-end, and then uses the hierarchical Transformer with Rotary Position Embedding (RoPE) to model the inner-band and inter-band sequences for multi-band mask estimation. This model has achieved state-of-the-art performance, but the band-split scheme is defined empirically, without analytic supports from the literature. In this paper, we propose Mel-RoFormer, which adopts the Mel-band scheme that maps the frequency bins into overlapped subbands according to the mel scale. In contract, the band-split mapping in BSRNN and BS-RoFormer is non-overlapping and designed based on heuristics. Using the MUSDB18HQ dataset for experiments, we demonstrate that Mel-RoFormer outperforms BS-RoFormer in the separation tasks of vocals, drums, and other stems.
Forward citations
Cited by 10 Pith papers
-
HoliDubber: Holistic Video Dubbing for Complex Acoustic Scenes via Text-Guided Audio Synthesis
HoliDubber introduces a patch-based autoregressive diffusion transformer for joint text-guided synthesis of speech and ambient audio in video dubbing, with a new benchmark showing outperformance over prior speech-only...
-
Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods
Presents the ATTM grand challenge with efficiency and performance tracks for text-to-music generation using a public instrumental music dataset, evaluated via FAD, CLAP, a new CCS metric, and subjective tests.
-
Modeling Music as a Time-Frequency Image: A 2D Tokenizer for Music Generation
BandTok tokenizes Mel-spectrograms as independent time-frequency band tokens from a single codebook and pairs it with 2D RoPE in an autoregressive model to improve music generation over residual multi-codebook tokenizers.
-
YingMusic-Singer: Controllable Singing Voice Synthesis with Flexible Lyric Manipulation and Annotation-free Melody Guidance
YingMusic-Singer-Plus is a diffusion model for singing voice synthesis that preserves melody from a reference clip while allowing flexible lyric changes without manual alignment, outperforming Vevo2 and introducing th...
-
MIDI-Informed Singing Accompaniment Generation in a Compositional Song Pipeline
MIDI-SAG generates consistent long-form singing accompaniments by feeding symbolic MIDI timing, chords, and structure labels into a compositional pipeline built from pre-trained modules.
-
StemFX: Learning Mixing Style Representations via Autoregressive FX Chain Prediction on Source-Separated Stems
StemFX predicts tokenized per-stem audio-effect chains with a jointly-trained Transformer encoder-decoder, beating contrastive and prior FX-encoding methods on effect-chain retrieval and real-mix style transfer.
-
Persian MusicGen: A Large-Scale Dataset and Culturally-Aware Generative Model for Persian Music
Introduces the first large-scale Persian music dataset and shows fine-tuned MusicGen produces compositions more aligned with Persian stylistic conventions via tag-based evaluation.
-
YingMusic-Singer: Controllable Singing Voice Synthesis with Flexible Lyric Manipulation and Annotation-free Melody Guidance
A diffusion SVS model with curriculum training and GRPO edits lyrics while preserving melody without manual alignment, outperforming Vevo2 on LyricEditBench.
-
Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods
The paper introduces the ATTM Grand Challenge with a CC-licensed instrumental subset of MTG-Jamendo, two tracks, and evaluation via FAD, CLAP, and a new Concept Coverage Score to support academic text-to-music research.
-
Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models
MSST unifies training, validation, and inference for many music source-separation architectures and reports small quality gains from TTA, ensembling, and related engineering techniques.
Discussion (0). Sign in to comment.