The work started with the design of a flexible system architecture. This was gradually implemented by the IA and already after the first 6 months a first end-to-end content workflow could be tested.
Afterwards the focus of the work shifted towards the improvement of the results achieved when analysing the podcast data. For this, several automatic speech recognition (ASR) technologies have been tested and benchmarked against the gold standard available commercially. This helped us select the best solution and which was then further adapted to the needs of the MIXTAPE use cases.
Following this, the platform, with its content intake pipeline has been deployed in a distributed environment, and several large content intake jobs have been triggered in order to identify common issues and bottlenecks. Several issues came out and have been fixed: the platform now is stable and consistent. Any podcast feed can be added to the scheduler to inject the data into the platform and can be updated through time by defining how frequently it has to be updated. In the final months of the project the IA has focused on developing a friendly and modern user interface for accessing the core search functionality of MIXTAPE.
Moreover, throughout the project the IA has successfully completed a number of in-house tailored training activities and was able to seamlessly become part of the company’s team. The IA and the supervisor actively participated in the core training sessions provide by EASME through Ernst and Young, focusing mainly on the innovation aspects of the project. Both the innovation associate and the supervisor passed the final examination and were awarded the innovation management certificate.
The project was disseminated and communicated through various actions. The project was also featured on the IN2 webpage (
https://in-two.com/projects/mixtape(s’ouvre dans une nouvelle fenêtre)). MIXTAPE was also featured on IN2’s yearly newsletter which has a reach of almost 7000 business and research contacts. A flyer and presentation of MIXTAPE were prepared and we participated and disseminated the project during 3 online events.
The main outcome of the project is a software system that is able to ingest podcast feeds, analyse them using state-of-the-art Machine Learning techniques, automatically extracting a full transcript and using this to augment the existing metadata of the podcast and index the content. Based on this improved data and index it is possible to offer an advanced search for podcast episodes based on topics of interest and keywords which goes beyond the existing capabilities offered by major podcast platforms like iTunes and Spotify. IN2 will pursue the market introduction of MIXTAPE as a new freemium SaaS service for consumers. The MIXTAPE search engine is available at:
https://rmxtape.xyz/(s’ouvre dans une nouvelle fenêtre)
