{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ONM5CJX7P6Z762JVXLJNA2M672","short_pith_number":"pith:ONM5CJX7","schema_version":"1.0","canonical_sha256":"7359d126ff7fb3ff6935bad2d0699efe8df908e300678710e05a7a7145781207","source":{"kind":"arxiv","id":"2108.03569","version":1},"attestation_state":"computed","paper":{"title":"Deep Single Shot Musical Instrument Identification using Scalograms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.AS","eess.SP"],"primary_cat":"cs.SD","authors_text":"Aniruddha Chandra, Arindam Dutta, Debdutta Chatterjee, Dibakar Sil","submitted_at":"2021-08-08T05:11:07Z","abstract_excerpt":"Musical Instrument Identification has for long had a reputation of being one of the most ill-posed problems in the field of Musical Information Retrieval(MIR). Despite several robust attempts to solve the problem, a timeline spanning over the last five odd decades, the problem remains an open conundrum. In this work, the authors take on a further complex version of the traditional problem statement. They attempt to solve the problem with minimal data available - one audio excerpt per class. We propose to use a convolutional Siamese network and a residual variant of the same to identify musical"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2108.03569","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2021-08-08T05:11:07Z","cross_cats_sorted":["eess.AS","eess.SP"],"title_canon_sha256":"732cc9ac9e9b73fa5b38dd18fb9a717109480cd4842686cb38a1b849539ff66d","abstract_canon_sha256":"7ea2cc1ca325c17339bd52e82339a6cf866def012ce5e86cc33e328663f70804"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:04:07.096847Z","signature_b64":"QDrlte2cU0WsEFQi40VLYbRB+/hMub2E6cd0puzqKluGSUAadSi1/JH1UuIB8jfyyMEJIn4kGw79BkC/lcp+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7359d126ff7fb3ff6935bad2d0699efe8df908e300678710e05a7a7145781207","last_reissued_at":"2026-07-05T03:04:07.096442Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:04:07.096442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Single Shot Musical Instrument Identification using Scalograms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.AS","eess.SP"],"primary_cat":"cs.SD","authors_text":"Aniruddha Chandra, Arindam Dutta, Debdutta Chatterjee, Dibakar Sil","submitted_at":"2021-08-08T05:11:07Z","abstract_excerpt":"Musical Instrument Identification has for long had a reputation of being one of the most ill-posed problems in the field of Musical Information Retrieval(MIR). Despite several robust attempts to solve the problem, a timeline spanning over the last five odd decades, the problem remains an open conundrum. In this work, the authors take on a further complex version of the traditional problem statement. They attempt to solve the problem with minimal data available - one audio excerpt per class. We propose to use a convolutional Siamese network and a residual variant of the same to identify musical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.03569","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.03569/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2108.03569","created_at":"2026-07-05T03:04:07.096508+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.03569v1","created_at":"2026-07-05T03:04:07.096508+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.03569","created_at":"2026-07-05T03:04:07.096508+00:00"},{"alias_kind":"pith_short_12","alias_value":"ONM5CJX7P6Z7","created_at":"2026-07-05T03:04:07.096508+00:00"},{"alias_kind":"pith_short_16","alias_value":"ONM5CJX7P6Z762JV","created_at":"2026-07-05T03:04:07.096508+00:00"},{"alias_kind":"pith_short_8","alias_value":"ONM5CJX7","created_at":"2026-07-05T03:04:07.096508+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672","json":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672.json","graph_json":"https://pith.science/api/pith-number/ONM5CJX7P6Z762JVXLJNA2M672/graph.json","events_json":"https://pith.science/api/pith-number/ONM5CJX7P6Z762JVXLJNA2M672/events.json","paper":"https://pith.science/paper/ONM5CJX7"},"agent_actions":{"view_html":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672","download_json":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672.json","view_paper":"https://pith.science/paper/ONM5CJX7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.03569&json=true","fetch_graph":"https://pith.science/api/pith-number/ONM5CJX7P6Z762JVXLJNA2M672/graph.json","fetch_events":"https://pith.science/api/pith-number/ONM5CJX7P6Z762JVXLJNA2M672/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672/action/storage_attestation","attest_author":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672/action/author_attestation","sign_citation":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672/action/citation_signature","submit_replication":"https://pith.science/pith/ONM5CJX7P6Z762JVXLJNA2M672/action/replication_record"}},"created_at":"2026-07-05T03:04:07.096508+00:00","updated_at":"2026-07-05T03:04:07.096508+00:00"}