{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-scale-sub-band-constant-q-transform","title":"Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder","arxiv_id":"2311.14957","date":"2023-11-25","proceeding":null,"authors":["Yicheng Gu","Xueyao Zhang","Liumeng Xue","Zhizheng Wu"],"abstract":"Generative Adversarial Network (GAN) based vocoders are superior in inference speed and synthesis quality when reconstructing an audible waveform from an acoustic representation. This study focuses on improving the discriminator to promote GAN-based vocoders. Most existing time-frequency-representation-based discriminators are rooted in Short-Time Fourier Transform (STFT), whose time-frequency resolution in a spectrogram is fixed, making it incompatible with signals like singing voices that require flexible attention for different frequency bands. Motivated by that, our study utilizes the Constant-Q Transform (CQT), which owns dynamic resolution among frequencies, contributing to a better modeling ability in pitch accuracy and harmonic tracking. Specifically, we propose a Multi-Scale Sub-Band CQT (MS-SB-CQT) Discriminator, which operates on the CQT spectrogram at multiple scales and performs sub-band processing according to different octaves. Experiments conducted on both speech and singing voices confirm the effectiveness of our proposed method. Moreover, we also verified that the CQT-based and the STFT-based discriminators could be complementary under joint training. Specifically, enhanced by the proposed MS-SB-CQT and the existing MS-STFT Discriminators, the MOS of HiFi-GAN can be boosted from 3.27 to 3.87 for seen singers and from 3.40 to 3.78 for unseen singers.","url_abs":"https://arxiv.org/abs/2311.14957v1","url_pdf":"https://arxiv.org/pdf/2311.14957v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"multi-scale-sub-band-constant-q-transform","repo_url":"https://github.com/nvidia/bigvgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.14957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14957"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nvidia/bigvgan","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":5,"ran_draft_wrong":2,"ran_honours":1,"unverified":3},"by_repo_kind":{"listed":{"samples":11,"ran":8,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"738365634499b120","entry":"discriminator_loss","repo":"nvidia/bigvgan","repo_kind":"listed","path":"loss.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"738365634499b120"}},{"code_sha256_prefix":"6bcad10a17d9164f","entry":"dynamic_range_compression_torch","repo":"nvidia/bigvgan","repo_kind":"listed","path":"meldataset.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/meldataset.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6bcad10a17d9164f"}},{"code_sha256_prefix":"95f8dc8b58c53187","entry":"feature_loss","repo":"nvidia/bigvgan","repo_kind":"listed","path":"loss.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"95f8dc8b58c53187"}},{"code_sha256_prefix":"aa1f22c3cec16a93","entry":"generator_loss","repo":"nvidia/bigvgan","repo_kind":"listed","path":"loss.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"aa1f22c3cec16a93"}},{"code_sha256_prefix":"a26f85d7c72ef39a","entry":"get_padding","repo":"nvidia/bigvgan","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a26f85d7c72ef39a"}},{"code_sha256_prefix":"4bd54657e0aaaf57","entry":"load_checkpoint","repo":"nvidia/bigvgan","repo_kind":"listed","path":"inference_e2e.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/inference_e2e.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4bd54657e0aaaf57"}},{"code_sha256_prefix":"7c4b5745aafce584","entry":"plot_spectrogram_clipped","repo":"nvidia/bigvgan","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7c4b5745aafce584"}},{"code_sha256_prefix":"404632cb4f4669da","entry":"scan_checkpoint","repo":"nvidia/bigvgan","repo_kind":"listed","path":"inference_e2e.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/inference_e2e.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"404632cb4f4669da"}},{"code_sha256_prefix":"be031b15a988a556","entry":"dynamic_range_compression","repo":"nvidia/bigvgan","repo_kind":"listed","path":"meldataset.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/meldataset.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"be031b15a988a556"}},{"code_sha256_prefix":"9e30e7d510c9d341","entry":"dynamic_range_decompression","repo":"nvidia/bigvgan","repo_kind":"listed","path":"meldataset.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/meldataset.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9e30e7d510c9d341"}},{"code_sha256_prefix":"8520e6e61b8bf789","entry":"plot_spectrogram","repo":"nvidia/bigvgan","repo_kind":"listed","path":"utils.py","file_url":"https://github.com/nvidia/bigvgan/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8520e6e61b8bf789"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}