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• Unable to split audio using easy_audio_trimmer

  27 juillet 2023, par Sana Wasim

  can we use the easy_audio_trimmer package to split an audio ? I tried using the ffmpeg but it is conflicting with the above package and not work.

  


  I tried splitting by using these functions and it gave an error at the FlutterFFmpeg() method and i cant find an alternative also the duration(filePath) in the command final durationResult = await flutterSound.duration(filePath) ; shows an error

  


  Future<void> _splitAudio() async {&#xA;    setState(() {&#xA;      _progressVisibility = true;&#xA;    });&#xA;&#xA;    // Get the application documents directory&#xA;    final appDocumentsDirectory = await getApplicationDocumentsDirectory();&#xA;&#xA;    // Get the input audio file path&#xA;    final inputAudioPath = widget.file.path;&#xA;&#xA;    // Get the output file names for the two parts&#xA;    final outputFileName1 = &#x27;split_audio_part1.mp3&#x27;;&#xA;    final outputFileName2 = &#x27;split_audio_part2.mp3&#x27;;&#xA;&#xA;    // Get the output file paths for the two parts&#xA;    final outputPath1 = &#x27;${appDocumentsDirectory.path}/$outputFileName1&#x27;;&#xA;    final outputPath2 = &#x27;${appDocumentsDirectory.path}/$outputFileName2&#x27;;&#xA;&#xA;    // Calculate the duration of the original audio&#xA;    final originalDuration = await _getAudioDuration(inputAudioPath);&#xA;&#xA;    // Calculate the durations of the two parts&#xA;    final part1Duration = _startValue;&#xA;    final part2Duration = originalDuration - _endValue;&#xA;&#xA;    // Construct the FFmpeg command to split the audio&#xA;    final ffmpeg = FlutterFFmpeg();&#xA;    final splitCommand = &#x27;-i $inputAudioPath -ss 0 -t $part1Duration -c copy $outputPath1 -ss $_endValue -t $part2Duration -c copy $outputPath2&#x27;;&#xA;&#xA;    try {&#xA;      // Execute the FFmpeg command to split the audio&#xA;      final int result = await ffmpeg.execute(splitCommand);&#xA;&#xA;      if (result == 0) {&#xA;        setState(() {&#xA;          _progressVisibility = false;&#xA;        });&#xA;        debugPrint(&#x27;Audio split successfully.&#x27;);&#xA;      } else {&#xA;        setState(() {&#xA;          _progressVisibility = false;&#xA;        });&#xA;        debugPrint(&#x27;Failed to split audio.&#x27;);&#xA;      }&#xA;    } catch (error) {&#xA;      setState(() {&#xA;        _progressVisibility = false;&#xA;      });&#xA;      debugPrint(&#x27;Error while splitting audio: $error&#x27;);&#xA;    }&#xA;  }&#xA;&#xA;  Future<int> _getAudioDuration(String filePath) async {&#xA;    final flutterSound = FlutterSound();&#xA;    final durationResult = await flutterSound.duration(filePath);&#xA;    return durationResult.inMilliseconds;&#xA;  }&#xA;</int></void>

  


  Dependencies

  


   path_provider: ^2.0.15
  ffmpeg_kit_flutter: ^5.1.0
  audioplayers: ^4.1.0
  flutter_sound: ^9.2.13


  


• FFMPEG : Converting from raw audio to audio/mp4 (audio is being converted with slow speed)

  29 décembre 2017, par Valdir

  If I convert from mp3 to mp4 directly everything works perfectly. But if I try to convert from raw pcm, the audio speed is slowed down.

  I’ve tried the following (this works) :

  ffmpeg -i mp3/1.mp3 -strict -2 final.mp4

  This doesn’t work as expected :

  ffmpeg -f s16le -i final.raw -strict -2 -r 26 final.mp4

  With the following output :

  Input #0, s16le, from 'final.raw':
  
    Duration: 00:08:37.38, bitrate: 705 kb/s
  
      Stream #0:0: Audio: pcm_s16le, 44100 Hz, 1 channels, s16, 705 kb/s
  
  File 'final.mp4' already exists. Overwrite ? [y/N] y
  
  Output #0, mp4, to 'final.mp4':
  
    Metadata:
  
      encoder         : Lavf56.40.101
  
      Stream #0:0: Audio: aac ([64][0][0][0] / 0x0040), 44100 Hz, mono, fltp, 128 kb/s
  
      Metadata:
  
        encoder         : Lavc56.60.100 aac
  
  Stream mapping:
  
    Stream #0:0 -> #0:0 (pcm_s16le (native) -> aac (native))
  
  Press [q] to stop, [?] for help
  
  size=    8273kB time=00:08:37.38 bitrate= 131.0kbits/s
  
  video:0kB audio:8185kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.073808%

  I’ve tried to set parameters like :

  ffmpeg -ar 44100 -f s16le -i final.raw -strict -2 -r 26 final.mp4

  With no luck.

  In order to get the PCM from mp3 I’m using nodejs lame decoder :

  var decoder = new lame.Decoder({
  
          channels: 2,
  
          bitDepth: 16,
  
          sampleRate: 44100,
  
          bitRate: 128,
  
          outSampleRate: 44100, // 22050
  
          mode: lame.STEREO
  
      });

• Cohort Analysis 101 : How-To, Examples & Top Tools

  13 novembre 2023, par Erin — Analytics Tips

  Imagine that a farmer is trying to figure out why certain hens are laying large brown eggs and others are laying average-sized white eggs.

  The farmer decides to group the hens into cohorts based on what kind of eggs they lay to make it easier to detect patterns in their day-to-day lives. After careful observation and analysis, she discovered that the hens laying big brown eggs ate more than the roost’s other hens.

  With this cohort analysis, the farmer deduced that a hen’s body weight directly corresponds to egg size. She can now develop a strategy to increase the body weight of her hens to sell more large brown eggs, which are very popular at the weekly farmers’ market.

  Cohort analysis has a myriad of applications in the world of web analytics. Like our farmer, you can use it to better understand user behaviour and reap the benefits of your efforts. This article will discuss the best practices for conducting an effective cohort analysis and compare the top cohort analysis tools for 2024. 

  What is cohort analysis ?

  By definition, cohort analysis refers to a technique where users are grouped based on shared characteristics or behaviours and then examined over a specified period.

  Think of it as a marketing superpower, enabling you to comprehend user behaviours, craft personalised campaigns and allocate resources wisely, ultimately resulting in improved performance and better ROI.

  Why does cohort analysis matter ?

  In web analytics, a cohort is a group of users who share a certain behaviour or characteristic. The goal of cohort analysis is to uncover patterns and compare the performance and behaviour of different cohorts over time.

  An example of a cohort is a group of users who made their first purchase during the holidays. By analysing this cohort, you could learn more about their behaviour and buying patterns. You may discover that this cohort is more likely to buy specific product categories as holiday gifts — you can then tailor future holiday marketing campaigns to include these categories. 

  Types of cohort analysis

  There are a few different types of notable cohorts : 

  1. Time-based cohorts are groups of users categorised by a specific time. The example of the farmer we went over at the beginning of this section is a great example of a time-based cohort.
  2. Acquisition cohorts are users acquired during a specific time frame, event or marketing channel. Analysing these cohorts can help you determine the value of different acquisition methods. 
  3. Behavioural cohorts consist of users who show similar patterns of behaviour. Examples include frequent purchases with your mobile app or digital content engagement. 
  4. Demographic cohorts share common demographic characteristics like age, gender, education level and income. 
  5. Churn cohorts are buyers who have cancelled a subscription/stopped using your service within a specific time frame. Analysing churn cohorts can help you understand why customers leave.
  6. Geographic cohorts are pretty self-explanatory — you can use them to tailor your marketing efforts to specific regions. 
  7. Customer journey cohorts are based on the buyer lifecycle — from acquisition to adoption to retention. 
  8. Product usage cohorts are buyers who use your product/service specifically (think basic users, power users or occasional users). 

  Best practices for conducting a cohort analysis 

  So, you’ve decided you want to understand your user base better but don’t know how to go about it. Perhaps you want to reduce churn and create a more engaging user experience. In this section, we’ll walk you through the dos and don’ts of conducting an effective cohort analysis. Remember that you should tailor your cohort analysis strategy for organisation-specific goals.

  

  1. Preparing for cohort analysis : 

  • • First, define specific goals you want your cohort analysis to achieve. Examples include improving conversion rates or reducing churn.
    • Choosing the right time frame will help you compare short-term vs. long-term data trends. 

  2. Creating effective cohorts : 

  • • Define your segmentation criteria — anything from demographics to location, purchase history or user engagement level. Narrowing in on your specific segments will make your cohort analysis more precise. 
    • It’s important to find a balance between cohort size and similarity. If your cohort is too small and diverse, you won’t be able to find specific behavioural patterns.

  3. Performing cohort analysis :

  • • • Study retention rates across cohorts to identify patterns in user behaviour and engagement over time. Pay special attention to cohorts with high retention or churn rates. 
      • Analysing cohorts can reveal interesting behavioural insights — how do specific cohorts interact with your website ? Do they have certain preferences ? Why ? 

  4. Visualising and interpreting data :

  • • Visualising your findings can be a great way to reveal patterns. Line charts can help you spot trends, while bar charts can help you compare cohorts.
    • Guide your analytics team on how to interpret patterns in cohort data. Watch for sudden drops or spikes and what they could mean. 

  5. Continue improving :

  • • User behaviour is constantly evolving, so be adaptable. Continuous tracking of user behaviour will help keep your strategies up to date. 
    • Encourage iterative analysis optimisation based on your findings. 

  

  The top cohort analysis tools for 2024

  In this section, we’ll go over the best cohort analysis tools for 2024, including their key features, cohort analysis dashboards, cost and pros and cons.

  1. Matomo

  

  Matomo is an open-source, GDPR-compliant web analytics solution that offers cohort analysis as a standard feature in Matomo Cloud and is available as a plugin for Matomo On-Premise. Pairing traditional web analytics with cohort analysis will help you gain even deeper insights into understanding user behaviour over time. 

  You can use the data you get from web analytics to identify patterns in user behaviour and target your marketing strategies to specific cohorts. 

  Key features

  • Matomo offers a cohorts table that lets you compare cohorts side-by-side, and it comes with a time series.
    • All core session and conversion metrics are also available in the Cohorts report.
  • Create custom segments based on demographics, geography, referral sources, acquisition date, device types or user behaviour. 
  • Matomo provides retention analysis so you can track how many users from a specific cohort return to your website and when. 
  • Flexibly analyse your cohorts with custom reports. Customise your reports by combining metrics and dimensions specific to different cohorts. 
  • Create cohorts based on events or interactions with your website. 
  • Intuitive, colour-coded data visualisation, so you can easily spot patterns.

  Pros

  • No setup is needed if you use the JavaScript tracker
  • You can fetch cohort without any limit
  • 100% accurate data, no AI or Machine Learning data filling, and without the use of data sampling

  Cons

  • Matomo On-Premise (self-hosted) is free, but advanced features come with additional charges
  • Servers and technical know-how are required for Matomo On-Premise. Alternatively, for those not ready for self-hosting, Matomo Cloud presents a more accessible option and starts at $19 per month.

  Price : 

  • Matomo Cloud : 21-day free trial, then starts at $19 per month (includes Cohorts).
  • Matomo On-Premise : Free to self-host ; Cohorts plugin : 30-day free trial, then $99 per year.

  2. Mixpanel

  

  Mixpanel is a product analytics tool designed to help teams better understand user behaviour. It is especially well-suited for analysing user behaviour on iOS and Android apps. It offers various cohort analytics features that can be used to identify patterns and engage your users. 

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Compare how different cohorts engage with your app with Mixpanel’s comparative analysis features.
  • Create interactive dashboards, charts and graphs to visualise data.
  • Mixpanel provides retention analysis tools to see how often users return to your product over time. 
  • Send targeted messages and notifications to specific cohorts to encourage user engagement, announce new features, etc. 
  • Track and analyse user behaviours within cohorts — understand how different types of users engage with your product.

  Pros

  • Easily export cohort analysis data for further analysis
  • Combined with Mixpanel reports, cohorts can be a powerful tool for improving your product

  Cons

  • With the free Mixpanel plan, you can’t save cohorts for future use
  • Enterprise-level pricing is expensive
  • Time-consuming cohort creation process

  Price : Free basic version. The growth version starts at £16/month.

  3. Amplitude

  

  Amplitude is another product analytics solution that can help businesses track user interactions across digital platforms. Amplitude offers a standard toolkit for in-depth cohort analysis.

  Key features

  • Create cohorts based on criteria such as sign-up date, first purchase date, referral source, geographic location, device type or another custom event/property. 
  • Conduct behavioural, time-based and retention analyses.
  • Create custom reports with custom data.
  • Segment cohorts further based on additional criteria and compare multiple cohorts side-by-side.

  Pros

  • Highly customisable and flexible
  • Quick and simple setup

  Cons

  • Steep learning curve — requires significant training 
  • Slow loading speed
  • High price point compared to other tools

  Price : Free basic version. Plus version starts at £40/month (billed annually).

  4. Kissmetrics

  

  Kissmetrics is a customer engagement automation platform that offers powerful analytics features. Kissmetrics provides behavioural analytics, segmentation and email campaign automation. 

  Key features

  • Create cohorts based on demographics, user behaviour, referral sources, events and specific time frames.
  • The user path tool provides path visualisation so you can identify common paths users take and spot abandonment points. 
  • Create and optimise conversion funnels.
  • Customise events, user properties, funnels, segments, cohorts and more.

  Pros

  • Powerful data visualisation options
  • Highly customisable

  Cons

  • Difficult to install
  • Not well-suited for small businesses
  • Limited integration with other tools

  Price : Starting at £21/month for 10k events (billed monthly).

  Improve your cohort analysis with Matomo

  When choosing a cohort analysis tool, consider factors such as the tool’s ease of integration with your existing systems, data accuracy, the flexibility it offers in defining cohorts, the comprehensiveness of reporting features, and its scalability to accommodate the growth of your data and analysis needs over time. Moreover, it’s essential to confirm GDPR compliance to uphold rigorous privacy standards. 

  If you’re ready to understand your user’s behaviour, take Matomo for a test drive. Paired with web analytics, this powerful combination can advance your marketing efforts. Start your 21-day free trial today — no credit card required.