{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:M4CVPPW3J4NYFP53SFSMTWQCPL","short_pith_number":"pith:M4CVPPW3","canonical_record":{"source":{"id":"1910.01463","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-10-01T04:59:24Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"fcb97f4c1917b1f3760637dfe7ecaf88695bb248cf07d48c9cfcdcdfd4a06623","abstract_canon_sha256":"253a0985aea900a9883ebf6afc39a61059938cd234c509745746ba7f84325d25"},"schema_version":"1.0"},"canonical_sha256":"670557bedb4f1b82bfbb9164c9da027aefadb1017305d12b0b2bc1d1aef635d8","source":{"kind":"arxiv","id":"1910.01463","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.01463","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"1910.01463v2","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01463","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"M4CVPPW3J4NY","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"M4CVPPW3J4NYFP53","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"M4CVPPW3","created_at":"2026-07-05T00:09:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:M4CVPPW3J4NYFP53SFSMTWQCPL","target":"record","payload":{"canonical_record":{"source":{"id":"1910.01463","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-10-01T04:59:24Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"fcb97f4c1917b1f3760637dfe7ecaf88695bb248cf07d48c9cfcdcdfd4a06623","abstract_canon_sha256":"253a0985aea900a9883ebf6afc39a61059938cd234c509745746ba7f84325d25"},"schema_version":"1.0"},"canonical_sha256":"670557bedb4f1b82bfbb9164c9da027aefadb1017305d12b0b2bc1d1aef635d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:09:52.654097Z","signature_b64":"vIQ3ZErKc6kIAM2rqEda0pvMiT+ACtkgW8DnG6ZsaaF2HdPDrwu3VbFY98DgqG5q0k6X89L/W9QeUhi3J2n5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"670557bedb4f1b82bfbb9164c9da027aefadb1017305d12b0b2bc1d1aef635d8","last_reissued_at":"2026-07-05T00:09:52.653727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:09:52.653727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.01463","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wlVKH2wgDQ8n8cBPSpiYwRQx/a6nwvgiUBw8Ec7wubIU1s6WFdpyBzjldbwHaNDNPvwO4i2OaLFIqdaRQOi6AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T10:59:34.150186Z"},"content_sha256":"32b4d6269b94fcb9ebb1a94a241cf1e065aae7e7a92bcddbec4808cb370d0fe5","schema_version":"1.0","event_id":"sha256:32b4d6269b94fcb9ebb1a94a241cf1e065aae7e7a92bcddbec4808cb370d0fe5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:M4CVPPW3J4NYFP53SFSMTWQCPL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Balamurali B. T., Dorien Herremans, Gemma Roig, Kin Wai Cheuk","submitted_at":"2019-10-01T04:59:24Z","abstract_excerpt":"We present an approach to tackle the speaker recognition problem using Triplet Neural Networks. Currently, the $i$-vector representation with probabilistic linear discriminant analysis (PLDA) is the most commonly used technique to solve this problem, due to high classification accuracy with a relatively short computation time. In this paper, we explore a neural network approach, namely Triplet Neural Networks (TNNs), to built a latent space for different classifiers to solve the Multi-Target Speaker Detection and Identification Challenge Evaluation 2018 (MCE 2018) dataset. This training set co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01463","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/1910.01463/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":"2026-07-05T00:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pgyUzabEV1SmyhFXhKgXYzzeuB+x1ZXv222Yuue1FMi9P1fQy7+8LyUwW34JBjMXEoFK5zUBTYuusETtMXdUAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T10:59:34.150875Z"},"content_sha256":"cc9cb85a43c6e5cb61d643f67d0847f59b00fa356f8b3891b990bf6d30c91ab6","schema_version":"1.0","event_id":"sha256:cc9cb85a43c6e5cb61d643f67d0847f59b00fa356f8b3891b990bf6d30c91ab6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M4CVPPW3J4NYFP53SFSMTWQCPL/bundle.json","state_url":"https://pith.science/pith/M4CVPPW3J4NYFP53SFSMTWQCPL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M4CVPPW3J4NYFP53SFSMTWQCPL/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":"2026-08-05T10:59:34Z","links":{"resolver":"https://pith.science/pith/M4CVPPW3J4NYFP53SFSMTWQCPL","bundle":"https://pith.science/pith/M4CVPPW3J4NYFP53SFSMTWQCPL/bundle.json","state":"https://pith.science/pith/M4CVPPW3J4NYFP53SFSMTWQCPL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M4CVPPW3J4NYFP53SFSMTWQCPL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:M4CVPPW3J4NYFP53SFSMTWQCPL","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":"253a0985aea900a9883ebf6afc39a61059938cd234c509745746ba7f84325d25","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-10-01T04:59:24Z","title_canon_sha256":"fcb97f4c1917b1f3760637dfe7ecaf88695bb248cf07d48c9cfcdcdfd4a06623"},"schema_version":"1.0","source":{"id":"1910.01463","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.01463","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"1910.01463v2","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01463","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"M4CVPPW3J4NY","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"M4CVPPW3J4NYFP53","created_at":"2026-07-05T00:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"M4CVPPW3","created_at":"2026-07-05T00:09:52Z"}],"graph_snapshots":[{"event_id":"sha256:cc9cb85a43c6e5cb61d643f67d0847f59b00fa356f8b3891b990bf6d30c91ab6","target":"graph","created_at":"2026-07-05T00:09:52Z","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/1910.01463/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present an approach to tackle the speaker recognition problem using Triplet Neural Networks. Currently, the $i$-vector representation with probabilistic linear discriminant analysis (PLDA) is the most commonly used technique to solve this problem, due to high classification accuracy with a relatively short computation time. In this paper, we explore a neural network approach, namely Triplet Neural Networks (TNNs), to built a latent space for different classifiers to solve the Multi-Target Speaker Detection and Identification Challenge Evaluation 2018 (MCE 2018) dataset. This training set co","authors_text":"Balamurali B. T., Dorien Herremans, Gemma Roig, Kin Wai Cheuk","cross_cats":["cs.LG","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-10-01T04:59:24Z","title":"Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01463","kind":"arxiv","version":2},"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":"sha256:32b4d6269b94fcb9ebb1a94a241cf1e065aae7e7a92bcddbec4808cb370d0fe5","target":"record","created_at":"2026-07-05T00:09:52Z","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":"253a0985aea900a9883ebf6afc39a61059938cd234c509745746ba7f84325d25","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-10-01T04:59:24Z","title_canon_sha256":"fcb97f4c1917b1f3760637dfe7ecaf88695bb248cf07d48c9cfcdcdfd4a06623"},"schema_version":"1.0","source":{"id":"1910.01463","kind":"arxiv","version":2}},"canonical_sha256":"670557bedb4f1b82bfbb9164c9da027aefadb1017305d12b0b2bc1d1aef635d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"670557bedb4f1b82bfbb9164c9da027aefadb1017305d12b0b2bc1d1aef635d8","first_computed_at":"2026-07-05T00:09:52.653727Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:09:52.653727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vIQ3ZErKc6kIAM2rqEda0pvMiT+ACtkgW8DnG6ZsaaF2HdPDrwu3VbFY98DgqG5q0k6X89L/W9QeUhi3J2n5Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:09:52.654097Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.01463","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32b4d6269b94fcb9ebb1a94a241cf1e065aae7e7a92bcddbec4808cb370d0fe5","sha256:cc9cb85a43c6e5cb61d643f67d0847f59b00fa356f8b3891b990bf6d30c91ab6"],"state_sha256":"529d3a3fe21df7cd872470fcc4fbf98ade59a1f22a713282643b77474e44f1c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aDR4t+fVO4MdB07BrqzHuLCDS7ajUuk94UzfQvX/9USkRjL7w+dB6dnYYKRxGX0ULHM6BA8AlSTksSkYTIoqCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T10:59:34.155495Z","bundle_sha256":"12c8d55db25f46ae1f0ff40dd171f50c9a15bb568dfaba0a9e5046cac6f21702"}}