{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QBWMQJD3XDXIBH3JVNNRJGJRK6","short_pith_number":"pith:QBWMQJD3","schema_version":"1.0","canonical_sha256":"806cc8247bb8ee809f69ab5b14993157bd5133950003739d94170d06a0f8ba8d","source":{"kind":"arxiv","id":"1611.09524","version":1},"attestation_state":"computed","paper":{"title":"Understanding Audio Pattern Using Convolutional Neural Network From Raw Waveforms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Juncheng Li, Samarjit Das, Shuhui Qu, Wei Dai","submitted_at":"2016-11-29T08:33:48Z","abstract_excerpt":"One key step in audio signal processing is to transform the raw signal into representations that are efficient for encoding the original information. Traditionally, people transform the audio into spectral representations, as a function of frequency, amplitude and phase transformation. In this work, we take a purely data-driven approach to understand the temporal dynamics of audio at the raw signal level. We maximize the information extracted from the raw signal through a deep convolutional neural network (CNN) model. Our CNN model is trained on the urbansound8k dataset. We discover that salie"},"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":"1611.09524","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2016-11-29T08:33:48Z","cross_cats_sorted":[],"title_canon_sha256":"9bbab19d514a3e9c77ef5c248ac2acae1383ef45a1967b2359b51923871ed25c","abstract_canon_sha256":"3fbf45a195232e60ea7f5c261e00b6c6a29fd38308b34d2d795660c0a7c46d62"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:18.183545Z","signature_b64":"/hxeKBEIp+6j4yHnEXAxXL0bDwv/n6smoX+rpCmOaEErSJLBC3GJnqC1grDH2mMfM6g77oFlUEwtsnGPyBjaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"806cc8247bb8ee809f69ab5b14993157bd5133950003739d94170d06a0f8ba8d","last_reissued_at":"2026-05-18T00:56:18.182806Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:18.182806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Understanding Audio Pattern Using Convolutional Neural Network From Raw Waveforms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Juncheng Li, Samarjit Das, Shuhui Qu, Wei Dai","submitted_at":"2016-11-29T08:33:48Z","abstract_excerpt":"One key step in audio signal processing is to transform the raw signal into representations that are efficient for encoding the original information. Traditionally, people transform the audio into spectral representations, as a function of frequency, amplitude and phase transformation. In this work, we take a purely data-driven approach to understand the temporal dynamics of audio at the raw signal level. We maximize the information extracted from the raw signal through a deep convolutional neural network (CNN) model. Our CNN model is trained on the urbansound8k dataset. We discover that salie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09524","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.09524","created_at":"2026-05-18T00:56:18.182924+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.09524v1","created_at":"2026-05-18T00:56:18.182924+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09524","created_at":"2026-05-18T00:56:18.182924+00:00"},{"alias_kind":"pith_short_12","alias_value":"QBWMQJD3XDXI","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"QBWMQJD3XDXIBH3J","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"QBWMQJD3","created_at":"2026-05-18T12:30:39.010887+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/QBWMQJD3XDXIBH3JVNNRJGJRK6","json":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6.json","graph_json":"https://pith.science/api/pith-number/QBWMQJD3XDXIBH3JVNNRJGJRK6/graph.json","events_json":"https://pith.science/api/pith-number/QBWMQJD3XDXIBH3JVNNRJGJRK6/events.json","paper":"https://pith.science/paper/QBWMQJD3"},"agent_actions":{"view_html":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6","download_json":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6.json","view_paper":"https://pith.science/paper/QBWMQJD3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.09524&json=true","fetch_graph":"https://pith.science/api/pith-number/QBWMQJD3XDXIBH3JVNNRJGJRK6/graph.json","fetch_events":"https://pith.science/api/pith-number/QBWMQJD3XDXIBH3JVNNRJGJRK6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6/action/storage_attestation","attest_author":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6/action/author_attestation","sign_citation":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6/action/citation_signature","submit_replication":"https://pith.science/pith/QBWMQJD3XDXIBH3JVNNRJGJRK6/action/replication_record"}},"created_at":"2026-05-18T00:56:18.182924+00:00","updated_at":"2026-05-18T00:56:18.182924+00:00"}