{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IHM3G3GGKWQN7RF5JINR7BAOBP","short_pith_number":"pith:IHM3G3GG","schema_version":"1.0","canonical_sha256":"41d9b36cc655a0dfc4bd4a1b1f840e0bc66a9761593c3adc8ef1861f83f4a0f0","source":{"kind":"arxiv","id":"2606.17775","version":1},"attestation_state":"computed","paper":{"title":"A Neuromorphic Trigger for Efficient Audio Event Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.SD","authors_text":"Benjamin Hatton, Luca Peres, Oliver Rhodes","submitted_at":"2026-06-16T10:48:32Z","abstract_excerpt":"Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger for audio event detection, based on a spiking neural network (SNN) that selectively gates input to downstream models. The proposed trigger acts as a low-cost front-end, identifying salient audio segments and forwarding only these to a more computationally intensive model for tasks such as classification. The trigger is implemented as a lightweight fully connected SNN and evaluated on two representative tasks: Anomalous Sound Detec"},"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":"2606.17775","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2026-06-16T10:48:32Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"2f0e3a6d704075ba7a1582e17fdd37c1737aa660ad0b5c8e4f843522a149ef33","abstract_canon_sha256":"f96813a21260664e6819d10e6e0e28a7d30895b33a654ba5f2f5a7f69e4adf0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:21.431690Z","signature_b64":"O0BlgEnMkdcpNuNIhrfxdTVnSgqSB1soboRVPf7ZbkAsoaKEKwOSfnUMsThcGxmep7e9XDTa8SBwea0hZ/2xCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41d9b36cc655a0dfc4bd4a1b1f840e0bc66a9761593c3adc8ef1861f83f4a0f0","last_reissued_at":"2026-06-19T16:10:21.431338Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:21.431338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Neuromorphic Trigger for Efficient Audio Event Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.SD","authors_text":"Benjamin Hatton, Luca Peres, Oliver Rhodes","submitted_at":"2026-06-16T10:48:32Z","abstract_excerpt":"Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger for audio event detection, based on a spiking neural network (SNN) that selectively gates input to downstream models. The proposed trigger acts as a low-cost front-end, identifying salient audio segments and forwarding only these to a more computationally intensive model for tasks such as classification. The trigger is implemented as a lightweight fully connected SNN and evaluated on two representative tasks: Anomalous Sound Detec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17775","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/2606.17775/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":"2606.17775","created_at":"2026-06-19T16:10:21.431400+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17775v1","created_at":"2026-06-19T16:10:21.431400+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17775","created_at":"2026-06-19T16:10:21.431400+00:00"},{"alias_kind":"pith_short_12","alias_value":"IHM3G3GGKWQN","created_at":"2026-06-19T16:10:21.431400+00:00"},{"alias_kind":"pith_short_16","alias_value":"IHM3G3GGKWQN7RF5","created_at":"2026-06-19T16:10:21.431400+00:00"},{"alias_kind":"pith_short_8","alias_value":"IHM3G3GG","created_at":"2026-06-19T16:10:21.431400+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/IHM3G3GGKWQN7RF5JINR7BAOBP","json":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP.json","graph_json":"https://pith.science/api/pith-number/IHM3G3GGKWQN7RF5JINR7BAOBP/graph.json","events_json":"https://pith.science/api/pith-number/IHM3G3GGKWQN7RF5JINR7BAOBP/events.json","paper":"https://pith.science/paper/IHM3G3GG"},"agent_actions":{"view_html":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP","download_json":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP.json","view_paper":"https://pith.science/paper/IHM3G3GG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17775&json=true","fetch_graph":"https://pith.science/api/pith-number/IHM3G3GGKWQN7RF5JINR7BAOBP/graph.json","fetch_events":"https://pith.science/api/pith-number/IHM3G3GGKWQN7RF5JINR7BAOBP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP/action/storage_attestation","attest_author":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP/action/author_attestation","sign_citation":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP/action/citation_signature","submit_replication":"https://pith.science/pith/IHM3G3GGKWQN7RF5JINR7BAOBP/action/replication_record"}},"created_at":"2026-06-19T16:10:21.431400+00:00","updated_at":"2026-06-19T16:10:21.431400+00:00"}