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Type:
Bug
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Resolution: Unresolved
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Priority:
Normal
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None
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Affects Version/s: None
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Component/s: Scan / Audio Fingerprinting
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None
As the length for fpcalc calculation is 120s, any track that contains silence for the first two minutes will not get an AcoustID back.
I did not go through the code for acoustid-server, but it looks the SILENCE_HASH gets likely extracted from the fingerprint and ignored: https://github.com/acoustid/acoustid-server/blob/master/acoustid/fingerprint.py#L30
An example (default 120 and full duration):
fpcalc 12.\ \[silence\]\ _\ When\ Stars\ No\ More\ Shine.flac DURATION=386 FINGERPRINT=AQADtEmUaEkSRZEGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA fpcalc -length 386 12.\ \[silence\]\ _\ When\ Stars\ No\ More\ Shine.flac DURATION=386 FINGERPRINT=AQAMGEmUaEkSRZEGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADw40dOnCAO3MWVFzkO9UKVx_CRG98aIbV4_PiHGx904QdvcHUWAu2OkzWeF-oYDMfx4zJxHod49MddfD9-PMwPczoU-mAkCX4wXzB0Hn7xI99hlcd54zNwI3xwHLsO_cF7HIwP_NDxCPlC7NNxxHiLQ38mPCcO5yd6yD9-XH-QK8czEpEe-FEUKaAeaD32VDrao5lUD3ryYz-CWhl4KWC44z4uIXyGZhoPHUcJw8px0IePH_WEQ-gFc4fzI5cywyrRhynO-GinY_-Rz-gfHNOPH_6gf4NxpYav4czRg9EO-9CoEf3ho8-L4z-uXOjyHDpxiAM_CV2E9nB-_Dke-MGPi4nRTUeZ40fqJBvCD42Zo784_IemzQ1uBTbKHUebB-L-oHo6vEOk5MtylNrxHMf2HCdzPCXyozrRCM8h_WgU5mh3lCTxG55WfDZ6SHQe9DhMXoGNwx_QsduIH0d17vgMH_Jw0JERHDeye3AHkWoqfLuQRRY19AmYMOLx6cI3GWeKMGCeHOeh3ngPJqtytEetQ8uOs-gHc8fh49KgjF-IQ46wHX2KF4119PiLM0IfsbB0vCK4t9APuTjFcNjuAmXnDtcx82iS06gWhzL4aMRvlMd2Gd-xtzidHY1-PMe5C1mONpJ3fCgPy8ihH8h-3DjuY15kXEuQ89ARUtTx5seHmsqxnUbJoN2OI-uD4wxhKTx-vC98HD7hE0eX7TieSzj6-Gh2HR96GKXx7CjZD2KcCv6RC4471DvOgwzpoTrMSMhFQnyPXMd34z7Og8-H-8JzbDKOfPDR58L-4AeTMEeVKVE67KwoHM0-oj4u3GjyjOgq7Dn-DzQu5Ud_-ESPQ47UoyXhTxPB6EPYCp6O6_CRH-Y-aDxio58-eA9AMcZxX_CLvYPVD_lxHYf44zJy4kcwnXh-4KfQwz-Ow92hH-4_nPhx_NAPHuFyVDtu-DglIjeUy0iPPfiN_-Cmdjh-4SfMF_uR-4PPB5qc52iSE30WDdexC_2hE72U4x3K5fAPTbtQHtQjEYd3DZaDo-gNKxoP_Th-5D3x4riM_sJp5EYPHz3Rxiv8wlF1aF-GSxIhpQ98Hhfx44KjDr-Lm3h2CPXxH19yoh25DpZZMHdF_McvdId4tMTxIpRuGPWC7ygs5UH440f5QfxQ-cMvVMcj_NiPM8IPKz1OScGzCFfyQsuOL8d53DnyHNUP6wiP5_ix48qh8TlE6IShNUKRVrpwuMaPw_mLNs9x7DL649rxCdcRPug0QSyiC8cP5ktR7zi8TghExUJ4avhxEv2F5jy-GGFyoSRxLkXJo9E9fEKfgfJxHl4kfMQDPyuEHx5-_PB66MdV-EbJ40F_SDp6otNeONbxHe4heF22HO0O9hJOHk4WCnmO-TP-wy-RK1kOnUcOnzgXYscl5MWV4HgnEUr_oMEeHreR8tgeAj6RoyEDncaPaOJxGj3h54gbHj9qRYc0EznKHta2GP08fMe5HP6OH_mP-rDyQ6NwC1eYo3l2oT8-otdieBzE2oJHKTn6zLhy40idBHmHo3nxWDBVaJ9xiSl6aHkgT1JQMg-cC-aPUurBvETzHjqPuJnQbUGTzzhP1EdDHVd45A8h3gJ35Gp6PAx-NCNFhI9xZei8ww9Mot_Qozk-yEdzHrngo1-Co3mC8BmuQ_sShCF3XBKaLSFOockdPBFC8miYFHKkSIiPP-itGLxxzmiYMDYuiTHeYHyR9sb34zv2Da7TGTr6PDjOMAb1Q3OF_miI58aPphJu3DOuB-704oMW3_iF-0KY_EHlCV_AEOF-BUevQNyzIacO7_gD38b1wNHRR-Cd4s7wXsjJXNAaZZgy5OmQS9fwrEEzQh_MDyF75EzRU4fXQddxHTmPCz8sHz_MXJjO4lnw40HJD8e1Gh9D5Jo49BfkB7GJ0qCHE1eOM0XjHqcSHlx69CImHz8ubniCP6gudNHxo01__Ecaqg-0Z0aeHEdzDr2SFt_RHeeJJzSOF86IWuyxIt9x52BIHlcWg56gnSy-HAi-zPhR9sUrobmSHHuCo9aPhssVhHaE5jweXFG3CVeOPj0ufUiPGxeP9Mcr-DTa6ITcIXwC_zg2PjJ6Xag--DgDHz3iE222QNSPiNKOL2i2YscfBa2CJlR4gDzi59BSp-h31OAvNOF2yK2IF0d97Efno-LgL0YP_TOK7_gfHD_hL7iPb9hu4Ti-C91x_OgPPzraQ9N_NGcu_Mgf9D8e4YePH-FCHdPjwlU-OD56VFSIKSfyXRauH_2RFzb6Hr-Lo1GP_sGRHy2P5kUosaAuHGKOPvhRiRIaXOjhfCKeQ8qMPtLxxUiP93iPsi9CG76hXzh1-Mcr9Dl-EtornP3wEPhw7Qp-SD9soTuu5egf6IXJ48uOFGnOo812PE-h3Ufz4TxOoxJ8cthzfDfEEx41fFrxI7eCajy6LCeafLiy4yeadZh-5Be-GNqZwzmPRyZ-sDtewtbx4Tqk49dw9mAs6cU5nEe4G8_gH11O0FFxH1eOsFngE1-PAz-8Hf9AGzf-45EabD1--Ce-4Tx0ws9SYJKl4Nvx7vi9oExkrbjAX_DR68aXB2UEEZcS5UhVeEdfC3l29A7ho5ZwRe1x9jJQSRu8o--OUzpk-mC0Hy-86cd19Cy-gPmFJvqOR9mhK7mwC82L_sSj5biIHs2O_cGlHo3yHH0u4bvBvBt8HGd26Jdx_Mj04yiPjjtOOBdy1RCP_UQ-bHdwHLfg7PCRr8f34K3B3_B0HD9sDSe64xNV4gg9KRQ-PKSNa8eVcfiPsz1y6PCP8Kil4-A_dDGOij_8HFNF9GUmpBd0KUjzoN6O3scmHicu-LrA6-hxHnk4J4EU-RSa5HiiHVqqKOiP_DBfXMql4GEdnM4QRoH2kOCF4svx4sfzoTyuD707-EUW4z9-4ZKItoeNH0e0H9qH8Cl8VJJ47JKIn-iJhu_w5XhpgXmO_MeVIj_hByEv_NBxG8eHHw6nmOjOQkue4UcuHe3hLbhYxEe9QzbBmT3yE4-PPqUEHztOpE-h_Qh1wd_R_pgGh9PRo4evTmiJ7NCNkE0J7ywqKsSjK0L4HZ4I7Xh6vIqBViGHJvh6SPSJHj5AXBBvHCZ-4vjhF-Jxzvjx-fBzXAFP6D1eHhY-5MeJX8bFHn5xGf3BbDgcHTl6Fj9UJ6twNOuOHvkN5mhevBZaDukHH7qUI_cs4iNuPMcPHy3-4ToRHj_k9HjhkfgR_YVzfEqCizpyHQ4d450MLb_wKHiOR87RHMWu47gF3mh2ob-O9-hxQVJRfDva48cDyoaE-riIfjB55BeOi7iRh-Zg_Tiheod7-LjxC_ygk_DR50PvIyd8_EfOwj_6HHoukGUQXjmMvzBJ_NTxIz90FnmEfjj6aLCeoz9xMfgR_rBmQNdxhcefBz6PM8d16OJgrsaPvHiaJLh1YnJ-_MJ5NEd4HrqzIf9QXxOaY8cPXEae4GmPD7UO6LiUFTtx3MaHB7conD90-BzR4-egE_rx45zwYyKPH9SFP9BeVMcmCrF44vB5HK-MaIUf9NBx4Svx4kf9CTcO6cePr8fxHfhxvMbI4zgOa_pRp8R4PD2yOket49gpPIiF5-h1xZBDCz9uoXnoFNUiEvqOf9CxQ3tg93CHHudxOh0OhsMNfTl8_Hgkwy8mQx3xsegP3MJzXDBuHIdO4joe4MVhnByE4ziO48eLBy9sQcRxHMcFyIeP4zgOvziuHXjx4wCO47gZ_AAO-Dh-wDhw_DgOWMdPGMeBA8eB4weO4wYO_DDg4zhwHMeHAwfw4zheHD924Me_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
Track above: https://musicbrainz.org/recording/a8c4e4e0-1679-4e18-87b2-c79e169d525e
There is probably no reasonable fix to be done in Picard (unless seeing the continuous silence's As and retrying with longer sample, then filtering the As from that).
Proper fix could be on the fpcalc, filtering the all longer silences with ffmpeg.