| Title |
IS24 Characterisation of large amounts of wav audio |
| Detailed description | SB holds large amounts of WAV audio (200Tb +) in different resolutions (ranging from 22Khz 16 bit to 96Khz 24 bit). Different resolutions have been choosen over the years for different reasons (equipment, budgets for storage space, quality of original media in digitisation). Before we ingest all these older collections into our new DOMS we need to do simple characterisation on the files to ensure to generate correct technical metadata (in PREMIS format) for the files. We know that cirtain collections that claim to hold only eg. 48Khz 16 bit files have files in other resolutions - most likely as a result of mis-operation of the digitisation equipment. |
| Scalability Challenge |
Large amounts of data (200Tbytes +). For simple characterisation not much CPU is required but a lot of I/O is needed. Some of the files are rather large (8Gbytes) - could be a problem for some characterisation tools (not problematic for tools that only reads header information and magic bytes) |
| Issue champion | Gry Elstrøm (SB) |
| Other interested parties |
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| Possible Solution approaches | Should be simple. 1. Find appropriate characterisation tool that supports wav in a suitable manner * XC*L framework seems to have good wav support - evaluate and test. 2. Ensure this tools runs within the SCAPE platform |
| Context | |
| Lessons Learned | |
| Training Needs | |
| Datasets | WAV with Danish Radio broadcasts, ripped audio CD’s, and SB in-house audio digitization |
| Solutions | SO06 Use Ffprobe to characterise audio+video SO06 Use Ffprobe to characterise audio+videoSO25 Rosetta v3.0 Implementation Integrated with DROID 6, JHOVE1, NLNZ tool and more... |
Evaluation
| Objectives | This is about scaleability and functionality |
| Success criteria | We will have a workflow that can process WAV (and BWF) files - also larger files up to 10Gb |
| Automatic measures | 1. Support for both WAV and BWF 2. Support for larger files - up to 10Gb 3. Process 2Tbytes of sample content in less than 24 hours 4. 100% of the files are identified correctly 5. 100% of the files gets useful and correct characterisation output |
| Manual assessment | 1. Sample checking of the generated characterisation output |
| Actual evaluations | links to acutual evaluations of this Issue/Scenario |
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