{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VZ4VGMI3GCBPKGXCWL22ZY5U6F","short_pith_number":"pith:VZ4VGMI3","canonical_record":{"source":{"id":"1709.02291","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-07T14:51:37Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"44f1eaf5ab5ace29c8d6dc68ca36a58d82f13dfdef8416fed1f9ebe482763627","abstract_canon_sha256":"7e0aef13d02516c3688111e5d3dcbc1d020acb620639fa922b3da7c013ff4c82"},"schema_version":"1.0"},"canonical_sha256":"ae7953311b3082f51ae2b2f5ace3b4f175a59b78294b1d5461f1191b7b27d704","source":{"kind":"arxiv","id":"1709.02291","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.02291","created_at":"2026-05-18T00:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"1709.02291v3","created_at":"2026-05-18T00:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02291","created_at":"2026-05-18T00:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"VZ4VGMI3GCBP","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VZ4VGMI3GCBPKGXC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VZ4VGMI3","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VZ4VGMI3GCBPKGXCWL22ZY5U6F","target":"record","payload":{"canonical_record":{"source":{"id":"1709.02291","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-07T14:51:37Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"44f1eaf5ab5ace29c8d6dc68ca36a58d82f13dfdef8416fed1f9ebe482763627","abstract_canon_sha256":"7e0aef13d02516c3688111e5d3dcbc1d020acb620639fa922b3da7c013ff4c82"},"schema_version":"1.0"},"canonical_sha256":"ae7953311b3082f51ae2b2f5ace3b4f175a59b78294b1d5461f1191b7b27d704","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:24.032134Z","signature_b64":"W4PUDEs64fLx6vNbLjDqCNX2XYW5Gm0w2llkplin4O1QxQlbYP65QmLdhP+csoUKzyQY1jc4DZMMgcJmJjk9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae7953311b3082f51ae2b2f5ace3b4f175a59b78294b1d5461f1191b7b27d704","last_reissued_at":"2026-05-18T00:05:24.031633Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:24.031633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.02291","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4RNIEua1khX5/5AvZwVRzQuOOmuUsKMKD0yDDuxQyCJ5OFSlIcf6VzVPGruT2fHRBUB/voNTkJ4Vd7aS0o+/BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T21:22:52.122038Z"},"content_sha256":"f7396ad1aee311b4820a5c71ab63cfe38b6d7b5272e8c085c8bffc001324adb9","schema_version":"1.0","event_id":"sha256:f7396ad1aee311b4820a5c71ab63cfe38b6d7b5272e8c085c8bffc001324adb9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VZ4VGMI3GCBPKGXCWL22ZY5U6F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Arthur Flexer, Monika Doerfler, Roswitha Bammer, Thomas Grill","submitted_at":"2017-09-07T14:51:37Z","abstract_excerpt":"When convolutional neural networks are used to tackle learning problems based on music or, more generally, time series data, raw one-dimensional data are commonly pre-processed to obtain spectrogram or mel-spectrogram coefficients, which are then used as input to the actual neural network. In this contribution, we investigate, both theoretically and experimentally, the influence of this pre-processing step on the network's performance and pose the question, whether replacing it by applying adaptive or learned filters directly to the raw data, can improve learning success. The theoretical resul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02291","kind":"arxiv","version":3},"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":"2026-05-18T00:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3WzM5u7LE9mMNggtO4Hpe816oZnFJ6Ozw4R2LraC6Q8yK7yRIVlxZelyJBMrHIv+5bUdcsRAi1yjfAtA5fRCAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T21:22:52.122721Z"},"content_sha256":"3c38e50135fbb654e7254a5fb14531af4987442e5c5cfa513b163315e044ff2e","schema_version":"1.0","event_id":"sha256:3c38e50135fbb654e7254a5fb14531af4987442e5c5cfa513b163315e044ff2e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F/bundle.json","state_url":"https://pith.science/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F/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":"2026-05-27T21:22:52Z","links":{"resolver":"https://pith.science/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F","bundle":"https://pith.science/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F/bundle.json","state":"https://pith.science/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VZ4VGMI3GCBPKGXCWL22ZY5U6F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VZ4VGMI3GCBPKGXCWL22ZY5U6F","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":"7e0aef13d02516c3688111e5d3dcbc1d020acb620639fa922b3da7c013ff4c82","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-07T14:51:37Z","title_canon_sha256":"44f1eaf5ab5ace29c8d6dc68ca36a58d82f13dfdef8416fed1f9ebe482763627"},"schema_version":"1.0","source":{"id":"1709.02291","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.02291","created_at":"2026-05-18T00:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"1709.02291v3","created_at":"2026-05-18T00:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02291","created_at":"2026-05-18T00:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"VZ4VGMI3GCBP","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VZ4VGMI3GCBPKGXC","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VZ4VGMI3","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:3c38e50135fbb654e7254a5fb14531af4987442e5c5cfa513b163315e044ff2e","target":"graph","created_at":"2026-05-18T00:05:24Z","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":"When convolutional neural networks are used to tackle learning problems based on music or, more generally, time series data, raw one-dimensional data are commonly pre-processed to obtain spectrogram or mel-spectrogram coefficients, which are then used as input to the actual neural network. In this contribution, we investigate, both theoretically and experimentally, the influence of this pre-processing step on the network's performance and pose the question, whether replacing it by applying adaptive or learned filters directly to the raw data, can improve learning success. The theoretical resul","authors_text":"Arthur Flexer, Monika Doerfler, Roswitha Bammer, Thomas Grill","cross_cats":["cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-07T14:51:37Z","title":"Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02291","kind":"arxiv","version":3},"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:f7396ad1aee311b4820a5c71ab63cfe38b6d7b5272e8c085c8bffc001324adb9","target":"record","created_at":"2026-05-18T00:05:24Z","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":"7e0aef13d02516c3688111e5d3dcbc1d020acb620639fa922b3da7c013ff4c82","cross_cats_sorted":["cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-07T14:51:37Z","title_canon_sha256":"44f1eaf5ab5ace29c8d6dc68ca36a58d82f13dfdef8416fed1f9ebe482763627"},"schema_version":"1.0","source":{"id":"1709.02291","kind":"arxiv","version":3}},"canonical_sha256":"ae7953311b3082f51ae2b2f5ace3b4f175a59b78294b1d5461f1191b7b27d704","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ae7953311b3082f51ae2b2f5ace3b4f175a59b78294b1d5461f1191b7b27d704","first_computed_at":"2026-05-18T00:05:24.031633Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:24.031633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W4PUDEs64fLx6vNbLjDqCNX2XYW5Gm0w2llkplin4O1QxQlbYP65QmLdhP+csoUKzyQY1jc4DZMMgcJmJjk9Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:24.032134Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.02291","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f7396ad1aee311b4820a5c71ab63cfe38b6d7b5272e8c085c8bffc001324adb9","sha256:3c38e50135fbb654e7254a5fb14531af4987442e5c5cfa513b163315e044ff2e"],"state_sha256":"d4e00ec64d5a17359405b9f571a8770c25348324b635205f2d26c1bf8dd5728b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x9ZUaZaeBQjUh2uKxNzsDp7RfAjEUSDXkEdzhXFaHguK4D4VU5FbV3uNyTo+7RtyNGJ7lG2ASRHAd2NnQbPrBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T21:22:52.126657Z","bundle_sha256":"438abb6880d699476af90758f5eb6cf652efa7205c603956c6996f245cfa1aaf"}}