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• FFmpeg muxing theora/vorbis unable to flush ?

  11 novembre 2013, par user2979732

  I'm pretty new to ffmpeg and it's confusing. I'm working on a basic muxer and have been spending over a week on this - I don't normally post as I solve 98% of my issues with google, but unable to get this one so far.

  The basis of my source is FFmpeg's own muxing.c example. When I try to force it using libvorbis for audio, and create "test.ogg" it demonstrates the same issues I'm having in my own derivation of muxing.c. The problem is with ogg/theora/vorbis. I'm forcing the use of audio codec like this :

  audio_st = add_stream(oc, &audio_codec, avcodec_find_encoder_by_name("libvorbis")->id);

  It seems the problem is in not setting audio pts in the muxing.c sample. There is a confusion in general about this, nobody apart from this guy didn't address what I am looking for http://webcache.googleusercontent.com/search?q=cache:6ml82RMN3YYJ:ffmpeg.org/pipermail/libav-user/2013-April/004304.html+&cd=4&hl=en&ct=clnk&gl=cz

  I couldn't find any answers to that naturally - like why don't they set the audio pts ? Laziness ? Not needed ? Do they believe all encoders will produce the pts for them(not true as seen below) ?

  Anyway, when you try muxing.c with mp4/libx264/forced libmp3lame all is fine, but the encoder says that "encoder did not produce valid pts, making some up.". However, it's silent with ogg/theora/vorbis, as if there were valid pts(?) but the result is no audio packets present in the stream(!), at least from what I saw using ffprobe. Which results in the video not being able to replay even, until you take out the empty audio stream. Then it plays the video, which shows that stream is fine.

  Coming to my original issue. I tried setting the pts on the audio frame you're sending to the encoder to fix that problem(this already sucks). I was unable to find a definite answer how to properly set the pts - that's the other big issue as I'm trying stuff which I'm not sure works. Anyway, in the end when setting "some" pts, this results in ogg with sound.

  if (frame->pts == AV_NOPTS_VALUE) frame->pts = audio_sync_opts;
  
  audio_sync_opts = frame->pts + frame->nb_samples;

  I'm aware I should probably use rescaling to adjust for the container time bases etc..if this was present/explained in ffmpeg's own sample I wouldn't have to guess now (as I'm stil not 100% sure about time base relationship between container and codec, I think container time base takes somehow over the codec one).

  My other problem is flushing - but that might have something to do with the screwed up pts. So I won't rather get into that in detail - the basic problem is, when I send finite number of audio frames, like 20, I get 2 packets only for example. From my understanding, I need to flush the rest of audio after all the encoding/muxing is done, which I managed to do with mp4/libx264/libmp3lame, but with ogg/theora/vorbis it doesn't flush. Why not, I have no idea.

  If someone could rework muxing.c into sending it finite number of audio / video frames - ie . not until duration > X, but until it sent 20 video & 100 audio frames(just an example). So that number of frames I have is important, not the video time I end up with. Then encode / mux all the frames - with proper video/audio pts, working with theora/ogg and flushing if needed, that would probably solve all of my issues. I'm sure for an expert ffmpeg'er modifying muxing.c addressing all those things would be a pretty quick exercise and could help more than 1 confused person.

  Thanks !

• Matomo Launches Global Partner Programme to Deepen Local Connections and Champion Ethical Analytics

  25 juin, par Matomo Core Team — Press Releases

  Matomo introduces a global Partner Programme designed to connect organisations with trusted local experts, advancing its commitment to privacy, data sovereignty, and localisation.

  Wellington, New Zealand – 25 June 2025 — Matomo, the leading web analytics platform, is
  proud to announce the launch of the Matomo Partner Programme. This new initiative marks a significant step in Matomo’s global growth strategy, bringing together a carefully selected
  network of expert partners to support customers with localised, high–trust analytics services
  rooted in shared values.
  
  As privacy concerns rise and organisations seek alternatives to mainstream analytics solutions, the need for regional expertise has never been more vital. The Matomo Partner Programme ensures that customers around the world are supported not just by a world–class platform, but by trusted local professionals who understand their specific regulatory, cultural, and business needs.
  
  “Matomo is evolving. As privacy regulations become more nuanced and the need for regional
  understanding grows, we’ve made localisation a central pillar of our strategy. Our partners are
  the key to helping customers navigate these complexities with confidence and care,” said
  Adam Taylor, Chief Operating Officer at Matomo.

  Local Experts, Global Values

  At the heart of the Matomo Partner Programme is a commitment to connect clients with local experts who live and breathe their markets. These partners are more than service
  providers—they’re trusted advisors who bring deep insight into their region’s privacy
  legislation, cultural norms, sector–specific requirements, and digital trends.
  
  The programme empowers partners to act as extensions of Matomo’s core teams :
  
  • As Customer Success allies, delivering personalised training, support, and technical
  services in local languages and time zones.

  • As Sales ambassadors, raising awareness of ethical analytics in both public and private

  sectors, where trust, compliance, and transparency are crucial.
  
  This decentralised, values–aligned approach ensures that every Matomo customer benefits
  from localised delivery with global consistency.

  A Programme Designed for Impactful Partnerships

  The Matomo Partner Programme is open to organisations who share a commitment to ethical, open-source analytics and can demonstrate :

  • Technical excellence in deploying, configuring, and supporting Matomo Analytics in diverse environments.
  • Deep market understanding, allowing them to tell the Matomo story in ways that
  resonate locally.
  • Commercial strength to position Matomo across key industries, particularly in sectors with complex compliance and data sovereignty demands.

  Partners who meet these standards will be recognised as ‘Official Matomo Partners’— a symbol of excellence, credibility, and shared purpose. With this status, they gain access to :

  • Brand alignment and trust : Strengthen credibility with clients by promoting their
  connection to Matomo and its globally respected ethical stance.
  • Go-to-market support : Access to qualified leads, joint marketing, and tools to scale their business in a privacy-first market.
  • Strategic collaboration : Early insights into the product roadmap and direct
  engagement with Matomo’s core team.
  • Meaningful local impact : Help regional organisations reclaim control of their data and embrace ethical analytics with confidence.

  Ethical Analytics for Today’s World
  

  Matomo was founded in 2007 with the belief that people should have full control over their data. As the first open–source web analytics platform of its kind, Matomo continues to challenge the dominance of opaque, centralised tools by offering a transparent and flexible alternative that puts users first.
  
  In today’s landscape—marked by increased regulatory scrutiny, data protection concerns, and rapid advancements in AI—Matomo’s approach is more relevant than ever. Open–source technology provides the adaptability organisations need to respond to local expectations while reinforcing digital trust with users.

  Whether it’s a government department, healthcare provider, educational institution, or
  commercial business—Matomo partners are on the ground, ready to help organisations
  transition to analytics that are not only powerful but principled.

• How HSBC and ING are transforming banking with AI

  9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

  We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

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  Meet the expert panel

  Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.

   

  Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.

   

  Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.

   

  Key findings from the webinar

  The discussion highlighted four essential elements for advancing analytics capabilities :

  1. Strong data foundations matter most

  “It doesn’t matter how good the AI model is. It is garbage in, garbage out,”

  Johri explained. Banks need robust data governance that works across different regulatory environments.

  2. Transform rather than tweak

  Li Mandri emphasised the need to reconsider entire processes :

  “We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”

  3. Bridge technical and business understanding

  Both leaders stressed the value of analytics translators who understand both technology and business needs.

  “We’re investing in this layer we call product leads,”

  Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.

  4. Consider production costs early

  Moving from proof-of-concept to production requires careful planning. As Johri noted :

  “The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”

  This includes :

  • Ongoing monitoring requirements
  • Maintenance needs
  • Regulatory compliance checks
  • Regular model updates

  Real-world applications

  ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.

  Customer experience enhancement

  The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.

  “We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”

  Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.

  The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.

  The bank also uses AI to personalise customer communications.

  “We’re using that to make certain campaigns more personalised, having a certain tone of voice,”

  noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.

  Operational efficiency transformation

  ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.

  “KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”

  Li Mandri explained. The bank developed an AI-powered system that :

  • Automates document verification
  • Flags potential compliance issues for human review
  • Maintains consistent standards across jurisdictions
  • Reduces processing time while improving accuracy

  This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.

  In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.

  Sustainable finance initiatives

  ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.

  “We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”

  The bank developed AI models that :

  • Analyse company sustainability metrics
  • Compare environmental performance against industry benchmarks
  • Assess transition plans for high-emission industries
  • Monitor ongoing compliance with sustainability commitments

  This system helps staff evaluate the environmental impact of potential deals quickly and accurately.

  “We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”

  Li Mandri noted.

  HSBC’s innovative approach

  Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.

  Environmental monitoring through advanced technology

  HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.

  “This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”

  Johri explained.

  The system provides several key capabilities :

  • Analysis of forest coverage and deforestation rates
  • Assessment of biodiversity impact in specific regions
  • Monitoring of environmental changes over time
  • Measurement of environmental risk in lending portfolios

  “We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”

  Johri noted. This technology enables HSBC to :

  • Make informed lending decisions
  • Monitor environmental commitments of borrowers
  • Support sustainability-linked lending programmes
  • Provide accurate environmental impact reporting

  Transforming document analysis

  HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.

  “Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”

  Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.

  The bank’s approach includes :

  • Natural language processing to understand complex documents
  • Machine learning models to extract relevant information
  • Validation systems to ensure accuracy
  • Integration with existing compliance frameworks

  “We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”

  This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.

  Building future-ready analytics capabilities

  Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.

  Developing clear governance frameworks

  “Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”

  Johri emphasised. Banks need to establish governance structures that :

  • Define acceptable uses for AI
  • Establish monitoring and control mechanisms
  • Ensure compliance with evolving regulations
  • Maintain transparency in AI decision-making

  Creating solutions that scale

  Li Mandri stressed the importance of building systems that grow with the organisation :

  “When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”

  Successful scaling requires :

  • Standard approaches to model development
  • Clear evaluation frameworks
  • Simple processes for model updates
  • Strong monitoring systems
  • Regular performance reviews

  Investing in people and skills

  Both leaders highlighted how important skilled people are to analytics success.

  “Having a good hiring strategy as well as creating that data literacy is really important,”

  Johri noted. Banks need to :

  • Develop comprehensive training programmes
  • Create clear career paths for analytics professionals
  • Foster collaboration between technical and business teams
  • Build internal expertise in emerging technologies

  Planning for the future

  Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :

  • Adapting to new privacy regulations
  • Making AI decisions more explainable
  • Improving data quality and governance
  • Strengthening cybersecurity measures

  Practical steps for financial institutions

  The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.

  Key steps for success

  Financial institutions looking to enhance their analytics capabilities should :

  1. Start with strong foundations
     • Invest in clear data governance frameworks
     • Set data quality standards
     • Build thorough documentation processes
     • Create transparent data tracking
  2. Think strategically about AI implementation
     • Focus on transformative rather than small changes
     • Consider the full costs of AI projects
     • Build solutions that can grow
     • Balance innovation with risk management
  3. Invest in people and processes
     • Develop internal analytics expertise
     • Create clear paths for career growth
     • Foster collaboration between technical and business teams
     • Build a culture of data literacy
  4. Plan for scale
     • Establish monitoring systems
     • Create governance frameworks
     • Develop standard approaches to model development
     • Stay flexible for future regulatory changes

  Learn more

  Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :

  • Detailed technical insights from both banks
  • Extended Q&A with the speakers
  • Additional case studies and examples
  • Practical implementation advice

   

   

  Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

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