{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TY6J2FR6CU6EXTXOXZZBC5NIIQ","short_pith_number":"pith:TY6J2FR6","canonical_record":{"source":{"id":"1712.03835","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-11T15:25:01Z","cross_cats_sorted":[],"title_canon_sha256":"2686e19823056b948637510122f59cfa21aa53cec88e225695893c625541832b","abstract_canon_sha256":"ccc078532cffe8b351d4d71ac85bd48bd8c21d124359a1ff51ad95bb3b48f9a9"},"schema_version":"1.0"},"canonical_sha256":"9e3c9d163e153c4bceeebe721175a8443a748657eb1d1906bdc9c90d113d5e38","source":{"kind":"arxiv","id":"1712.03835","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.03835","created_at":"2026-05-18T00:28:19Z"},{"alias_kind":"arxiv_version","alias_value":"1712.03835v1","created_at":"2026-05-18T00:28:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03835","created_at":"2026-05-18T00:28:19Z"},{"alias_kind":"pith_short_12","alias_value":"TY6J2FR6CU6E","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TY6J2FR6CU6EXTXO","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TY6J2FR6","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TY6J2FR6CU6EXTXOXZZBC5NIIQ","target":"record","payload":{"canonical_record":{"source":{"id":"1712.03835","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-11T15:25:01Z","cross_cats_sorted":[],"title_canon_sha256":"2686e19823056b948637510122f59cfa21aa53cec88e225695893c625541832b","abstract_canon_sha256":"ccc078532cffe8b351d4d71ac85bd48bd8c21d124359a1ff51ad95bb3b48f9a9"},"schema_version":"1.0"},"canonical_sha256":"9e3c9d163e153c4bceeebe721175a8443a748657eb1d1906bdc9c90d113d5e38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:19.808870Z","signature_b64":"+pl5la9uyZNgK/mn7I/eFDam4PDC34HcuoRHQXFfB2r8cD3/UNKjBy42EFauy3yjrJqnsfajEFbXwBqwfN98DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e3c9d163e153c4bceeebe721175a8443a748657eb1d1906bdc9c90d113d5e38","last_reissued_at":"2026-05-18T00:28:19.808127Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:19.808127Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.03835","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":"2026-05-18T00:28:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2UKgZBDptbiK9oLBQLQ+I0tB2QODTppsgJieWE9Xh9/J2SDKrmcWMY+SDp3/AXgB3gQyCDvto7kytH+IyextDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T19:18:19.118622Z"},"content_sha256":"69aa9b5dbd44c30c11d5cdcb569e037e9659b2c3906fae2d44b71cef34ed6919","schema_version":"1.0","event_id":"sha256:69aa9b5dbd44c30c11d5cdcb569e037e9659b2c3906fae2d44b71cef34ed6919"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TY6J2FR6CU6EXTXOXZZBC5NIIQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Feature Learning for Audio Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jan Beutel, Lothar Thiele, Matthias Meyer","submitted_at":"2017-12-11T15:25:01Z","abstract_excerpt":"Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method for exploration of audio data is presented in this paper. It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction. Second, a training method"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.03835","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":""},"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":"2026-05-18T00:28:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HUA7Zbe0V8clfCqbcZFhb0/00dAIFrj2GaNaATgUWnBXan7Hzsdj9c0xsLlhiq/PkxyNbNiPFoFUrXLqs85iAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T19:18:19.118968Z"},"content_sha256":"2d9915c8914479f617e3eab3021933dd32a927caa060c3c58dad8cf8cf68bb5e","schema_version":"1.0","event_id":"sha256:2d9915c8914479f617e3eab3021933dd32a927caa060c3c58dad8cf8cf68bb5e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ/bundle.json","state_url":"https://pith.science/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ/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":"2026-06-23T19:18:19Z","links":{"resolver":"https://pith.science/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ","bundle":"https://pith.science/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ/bundle.json","state":"https://pith.science/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TY6J2FR6CU6EXTXOXZZBC5NIIQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TY6J2FR6CU6EXTXOXZZBC5NIIQ","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":"ccc078532cffe8b351d4d71ac85bd48bd8c21d124359a1ff51ad95bb3b48f9a9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-11T15:25:01Z","title_canon_sha256":"2686e19823056b948637510122f59cfa21aa53cec88e225695893c625541832b"},"schema_version":"1.0","source":{"id":"1712.03835","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.03835","created_at":"2026-05-18T00:28:19Z"},{"alias_kind":"arxiv_version","alias_value":"1712.03835v1","created_at":"2026-05-18T00:28:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.03835","created_at":"2026-05-18T00:28:19Z"},{"alias_kind":"pith_short_12","alias_value":"TY6J2FR6CU6E","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TY6J2FR6CU6EXTXO","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TY6J2FR6","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:2d9915c8914479f617e3eab3021933dd32a927caa060c3c58dad8cf8cf68bb5e","target":"graph","created_at":"2026-05-18T00:28:19Z","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"},"paper":{"abstract_excerpt":"Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method for exploration of audio data is presented in this paper. It incorporates the two following novel contributions: First, an audio frame predictor based on a Convolutional LSTM autoencoder is demonstrated, which is used for unsupervised feature extraction. Second, a training method","authors_text":"Jan Beutel, Lothar Thiele, Matthias Meyer","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-11T15:25:01Z","title":"Unsupervised Feature Learning for Audio Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.03835","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":"sha256:69aa9b5dbd44c30c11d5cdcb569e037e9659b2c3906fae2d44b71cef34ed6919","target":"record","created_at":"2026-05-18T00:28:19Z","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":"ccc078532cffe8b351d4d71ac85bd48bd8c21d124359a1ff51ad95bb3b48f9a9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-11T15:25:01Z","title_canon_sha256":"2686e19823056b948637510122f59cfa21aa53cec88e225695893c625541832b"},"schema_version":"1.0","source":{"id":"1712.03835","kind":"arxiv","version":1}},"canonical_sha256":"9e3c9d163e153c4bceeebe721175a8443a748657eb1d1906bdc9c90d113d5e38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9e3c9d163e153c4bceeebe721175a8443a748657eb1d1906bdc9c90d113d5e38","first_computed_at":"2026-05-18T00:28:19.808127Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:19.808127Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+pl5la9uyZNgK/mn7I/eFDam4PDC34HcuoRHQXFfB2r8cD3/UNKjBy42EFauy3yjrJqnsfajEFbXwBqwfN98DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:19.808870Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.03835","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:69aa9b5dbd44c30c11d5cdcb569e037e9659b2c3906fae2d44b71cef34ed6919","sha256:2d9915c8914479f617e3eab3021933dd32a927caa060c3c58dad8cf8cf68bb5e"],"state_sha256":"150fe66a165d68a819648d36b5fdeb9c2845a3fcc8a3fec9b9acdb0a663eab0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j5DUZA8mvsfqNU8ijUJhsn+IK0qPzq8tNRYv89n8vv5U2dJvWVnD0LvlFqeF47Am6vl/syL/DBRMApW5FrKHCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T19:18:19.120928Z","bundle_sha256":"f9721201f6937fe338fe5a70e7f6c2dfab698b22879d92ad868d69de16f9b192"}}