{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XQMSVTPDQLM4DKUREJS57ZBFFB","short_pith_number":"pith:XQMSVTPD","canonical_record":{"source":{"id":"2506.18406","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2025-06-23T08:42:28Z","cross_cats_sorted":[],"title_canon_sha256":"34f9f556442580b8e5287a872eaa79a1cf82c5b6d0002605fd5917eef6eab98c","abstract_canon_sha256":"d4d75339670497914fc1b47bd0d74615d037ee20ab479d8f3690fcd1effe35af"},"schema_version":"1.0"},"canonical_sha256":"bc192acde382d9c1aa912265dfe425284adb733401e2f6e1acd726e70b85b314","source":{"kind":"arxiv","id":"2506.18406","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18406","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18406v1","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18406","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"pith_short_12","alias_value":"XQMSVTPDQLM4","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"pith_short_16","alias_value":"XQMSVTPDQLM4DKUR","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"pith_short_8","alias_value":"XQMSVTPD","created_at":"2026-07-05T11:25:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XQMSVTPDQLM4DKUREJS57ZBFFB","target":"record","payload":{"canonical_record":{"source":{"id":"2506.18406","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2025-06-23T08:42:28Z","cross_cats_sorted":[],"title_canon_sha256":"34f9f556442580b8e5287a872eaa79a1cf82c5b6d0002605fd5917eef6eab98c","abstract_canon_sha256":"d4d75339670497914fc1b47bd0d74615d037ee20ab479d8f3690fcd1effe35af"},"schema_version":"1.0"},"canonical_sha256":"bc192acde382d9c1aa912265dfe425284adb733401e2f6e1acd726e70b85b314","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:49.816534Z","signature_b64":"YzO5N3IrV8jHm33hfIRNq7TVNDfz08txS11dmgjd5HsAkj5c8zVizM2o/WX7cO2PwCls2kHX9ZqP/sOyv6A3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc192acde382d9c1aa912265dfe425284adb733401e2f6e1acd726e70b85b314","last_reissued_at":"2026-07-05T11:25:49.816059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:49.816059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.18406","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4h7Szi9qWfSOTWvcvzv55RrxUkAsRB8qVYz9W2DlJ0pF2mGGyMIRSOA2yCNkzG93Z6/JLC5kgbzsKoEZSDcGDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:57:37.823352Z"},"content_sha256":"67a2f2897ccaef271106f328db658b3116b2c12b20da8223bdcdc8790f80808c","schema_version":"1.0","event_id":"sha256:67a2f2897ccaef271106f328db658b3116b2c12b20da8223bdcdc8790f80808c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XQMSVTPDQLM4DKUREJS57ZBFFB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Il-Youp Kwak, Jiaxin Tan, Qianhua He, Yanxiong Li, Yongjie Si","submitted_at":"2025-06-23T08:42:28Z","abstract_excerpt":"In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many base classes due to data scarcity and high collection cost. We discuss a more realistic issue, Fully FCAC (FFCAC), in which training samples of both base and incremental classes are only a few. Furthermore, we propose a FFCAC method using a model which is decoupled into a multi-level embedding extractor and a ridge regression classifier. The embedding extractor consists of an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18406","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/2506.18406/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:25:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GnO0pm8d8ERjNkKvtV1Sp2u7MCa0Bv5yVmimDVo8+TRqNHD81bRtoZ+WVzeW7dOO3Z66WaW5myE5823SVxGkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T17:57:37.823727Z"},"content_sha256":"905d96de57521431b3f00600a1814253aa1357dea441305c111a197506941483","schema_version":"1.0","event_id":"sha256:905d96de57521431b3f00600a1814253aa1357dea441305c111a197506941483"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XQMSVTPDQLM4DKUREJS57ZBFFB/bundle.json","state_url":"https://pith.science/pith/XQMSVTPDQLM4DKUREJS57ZBFFB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XQMSVTPDQLM4DKUREJS57ZBFFB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T17:57:37Z","links":{"resolver":"https://pith.science/pith/XQMSVTPDQLM4DKUREJS57ZBFFB","bundle":"https://pith.science/pith/XQMSVTPDQLM4DKUREJS57ZBFFB/bundle.json","state":"https://pith.science/pith/XQMSVTPDQLM4DKUREJS57ZBFFB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XQMSVTPDQLM4DKUREJS57ZBFFB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XQMSVTPDQLM4DKUREJS57ZBFFB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d4d75339670497914fc1b47bd0d74615d037ee20ab479d8f3690fcd1effe35af","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2025-06-23T08:42:28Z","title_canon_sha256":"34f9f556442580b8e5287a872eaa79a1cf82c5b6d0002605fd5917eef6eab98c"},"schema_version":"1.0","source":{"id":"2506.18406","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.18406","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"arxiv_version","alias_value":"2506.18406v1","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.18406","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"pith_short_12","alias_value":"XQMSVTPDQLM4","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"pith_short_16","alias_value":"XQMSVTPDQLM4DKUR","created_at":"2026-07-05T11:25:49Z"},{"alias_kind":"pith_short_8","alias_value":"XQMSVTPD","created_at":"2026-07-05T11:25:49Z"}],"graph_snapshots":[{"event_id":"sha256:905d96de57521431b3f00600a1814253aa1357dea441305c111a197506941483","target":"graph","created_at":"2026-07-05T11:25:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.18406/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many base classes due to data scarcity and high collection cost. We discuss a more realistic issue, Fully FCAC (FFCAC), in which training samples of both base and incremental classes are only a few. Furthermore, we propose a FFCAC method using a model which is decoupled into a multi-level embedding extractor and a ridge regression classifier. The embedding extractor consists of an ","authors_text":"Il-Youp Kwak, Jiaxin Tan, Qianhua He, Yanxiong Li, Yongjie Si","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2025-06-23T08:42:28Z","title":"Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.18406","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:67a2f2897ccaef271106f328db658b3116b2c12b20da8223bdcdc8790f80808c","target":"record","created_at":"2026-07-05T11:25:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d4d75339670497914fc1b47bd0d74615d037ee20ab479d8f3690fcd1effe35af","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2025-06-23T08:42:28Z","title_canon_sha256":"34f9f556442580b8e5287a872eaa79a1cf82c5b6d0002605fd5917eef6eab98c"},"schema_version":"1.0","source":{"id":"2506.18406","kind":"arxiv","version":1}},"canonical_sha256":"bc192acde382d9c1aa912265dfe425284adb733401e2f6e1acd726e70b85b314","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc192acde382d9c1aa912265dfe425284adb733401e2f6e1acd726e70b85b314","first_computed_at":"2026-07-05T11:25:49.816059Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:49.816059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YzO5N3IrV8jHm33hfIRNq7TVNDfz08txS11dmgjd5HsAkj5c8zVizM2o/WX7cO2PwCls2kHX9ZqP/sOyv6A3Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:49.816534Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.18406","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67a2f2897ccaef271106f328db658b3116b2c12b20da8223bdcdc8790f80808c","sha256:905d96de57521431b3f00600a1814253aa1357dea441305c111a197506941483"],"state_sha256":"8984073323c9eee163f536fede873f72f8dec5103c63d22b08e02b1f34955435"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4BvLs0whSfIMCBUi1DH6G3L54h0Z5blGuiH0/uMDCRJlsfRjaTow7zdkcVbKzG0GLL1NWl4Hu8IaJrXsyo/fAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T17:57:37.826570Z","bundle_sha256":"8d66fd55804f026b693d804bb4c297229783c4271e16c095161c48722fce63f4"}}