{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:B6R6T2JHAHCOPWCVI3EBJV6VUD","short_pith_number":"pith:B6R6T2JH","schema_version":"1.0","canonical_sha256":"0fa3e9e92701c4e7d85546c814d7d5a0e1939eea747165e4c962cfc01f719963","source":{"kind":"arxiv","id":"2407.00465","version":2},"attestation_state":"computed","paper":{"title":"Characterizing Continual Learning Scenarios and Strategies for Audio Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Abdulmotaleb El Saddik, Dwarikanath Mahapatra, Mukesh Saini, Pratibha Kumari, Ruchi Bhatt","submitted_at":"2024-06-29T15:21:20Z","abstract_excerpt":"Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume the data distribution at training and deployment time will be the same. However, due to various real-life challenges, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. Continual learning (CL) approaches are devised to handle such changes in data distribution. There have been a few attempts to use CL approaches for audio analysis. Yet, there is a lack of a systematic evaluation f"},"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":"2407.00465","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-06-29T15:21:20Z","cross_cats_sorted":["cs.CV","cs.LG","eess.AS"],"title_canon_sha256":"56d7843e741e554984b7af586ab0d6d04b8119c4568e2197ca0c68c7bad3f045","abstract_canon_sha256":"61c5d3abb52624f0d72027fed8e1a32b0a4a04ab54b32fea2de20406680c995d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:45.099651Z","signature_b64":"NovvDAIrXDK54MPcUAZp5sMIToz/o9mdF3WFHlkXp9m9YPHqISwjJ1mX1Pw8rn9y6UeRG0U+eI+WoFbHK9K5Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fa3e9e92701c4e7d85546c814d7d5a0e1939eea747165e4c962cfc01f719963","last_reissued_at":"2026-07-05T08:48:45.099211Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:45.099211Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characterizing Continual Learning Scenarios and Strategies for Audio Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Abdulmotaleb El Saddik, Dwarikanath Mahapatra, Mukesh Saini, Pratibha Kumari, Ruchi Bhatt","submitted_at":"2024-06-29T15:21:20Z","abstract_excerpt":"Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume the data distribution at training and deployment time will be the same. However, due to various real-life challenges, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. Continual learning (CL) approaches are devised to handle such changes in data distribution. There have been a few attempts to use CL approaches for audio analysis. Yet, there is a lack of a systematic evaluation f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00465","kind":"arxiv","version":2},"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/2407.00465/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":"2407.00465","created_at":"2026-07-05T08:48:45.099268+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.00465v2","created_at":"2026-07-05T08:48:45.099268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00465","created_at":"2026-07-05T08:48:45.099268+00:00"},{"alias_kind":"pith_short_12","alias_value":"B6R6T2JHAHCO","created_at":"2026-07-05T08:48:45.099268+00:00"},{"alias_kind":"pith_short_16","alias_value":"B6R6T2JHAHCOPWCV","created_at":"2026-07-05T08:48:45.099268+00:00"},{"alias_kind":"pith_short_8","alias_value":"B6R6T2JH","created_at":"2026-07-05T08:48:45.099268+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.07711","citing_title":"RenderBox: Expressive Performance Rendering with Text Control","ref_index":2023,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD","json":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD.json","graph_json":"https://pith.science/api/pith-number/B6R6T2JHAHCOPWCVI3EBJV6VUD/graph.json","events_json":"https://pith.science/api/pith-number/B6R6T2JHAHCOPWCVI3EBJV6VUD/events.json","paper":"https://pith.science/paper/B6R6T2JH"},"agent_actions":{"view_html":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD","download_json":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD.json","view_paper":"https://pith.science/paper/B6R6T2JH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.00465&json=true","fetch_graph":"https://pith.science/api/pith-number/B6R6T2JHAHCOPWCVI3EBJV6VUD/graph.json","fetch_events":"https://pith.science/api/pith-number/B6R6T2JHAHCOPWCVI3EBJV6VUD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD/action/storage_attestation","attest_author":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD/action/author_attestation","sign_citation":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD/action/citation_signature","submit_replication":"https://pith.science/pith/B6R6T2JHAHCOPWCVI3EBJV6VUD/action/replication_record"}},"created_at":"2026-07-05T08:48:45.099268+00:00","updated_at":"2026-07-05T08:48:45.099268+00:00"}