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• Why is there an audio delay on recording video stream with ffmpeg?

  25 December 2023, by mqwerty

  I am trying to record video and audio stream (Line in Microphone Analog Audio) which are streaming from broadcaster computer with those parameters in the recorder computer ;

  


  ffmpeg record parameters :

  


  /usr/bin/ffmpeg -y -buffer_size max -thread_queue_size 8192 -i udp://225.0.5.11:1026 -buffer_size max -thread_queue_size 8192 -i udp://225.0.5.11:1032 -map 0:v -map 1:a -metadata title=COMPUTER-01_metadata_file -metadata creation_time="2023-12-25 13:25:29" -threads 0 -c:v copy -c:a copy -movflags +faststart -f segment -segment_time 01:00:00 -segment_atclocktime 1 -reset_timestamps 1 -strftime 1 -segment_format mp4 -t 120 test_record_video_with_audio_%Y-%m-%d_%H-%M-%S.mp4


  


  The ffmpeg started and finished successfully, but when I open the recorded video with mpv like (mpv test_record_video_with_audio.mp4), I realized that there is a 5-6 seconds delay in audio. How can I prevent the delay of audio in the recorded mp4 file without using offset ? My last option is setting offset but I think that it is not safe according to any changes in network or etc.

  


  FFMPEG version on both computer :

  


  ffmpeg version 4.2.9 Copyright (c) 2000-2023 the FFmpeg developers
built with gcc 8 (GCC)


  


  BROADCASTER COMPUTER :

  


  sysctl.conf:

  


  No added configurations.


  


  ethtool output :

  


  Supported ports: [ TP ]
Supported link modes:   100baseT/Full
                        1000baseT/Full
                        10000baseT/Full
                        2500baseT/Full
                        5000baseT/Full
Supported pause frame use: Symmetric
Supports auto-negotiation: Yes
Supported FEC modes: Not reported
Advertised link modes:  100baseT/Full
                        1000baseT/Full
                        10000baseT/Full
Advertised pause frame use: Symmetric
Advertised auto-negotiation: Yes
Advertised FEC modes: Not reported
Speed: 10000Mb/s
Duplex: Full
Auto-negotiation: on
Port: Twisted Pair
PHYAD: 0
Transceiver: internal
MDI-X: Unknown
Supports Wake-on: d
Wake-on: d
    Current message level: 0x00000007 (7)
                           drv probe link
Link detected: yes


  


  ffmpeg video stream :

  


  ffmpeg -fflags +genpts -f x11grab -framerate 30 -video_size uhd2160 -i :0 -c:v hevc_nvenc -preset fast -pix_fmt bgr0 -b:v 3M -g 25 -an -f mpegts udp://225.0.5.11:1026


  


  ffmpeg audio stream :

  


  ffmpeg -f alsa -i hw:0,0 -c:a aac -ar 48000 -b:a 1024K -ab 512k -f rtp_mpegts rtp://225.0.5.11:1032


  


  nvidia-smi :

  


  | NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA T400 4GB                Off | 00000000:5B:00.0 Off |                  N/A |
| 38%   38C    P8              N/A /  31W |    207MiB /  4096MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA RTX A4000               Off | 00000000:9E:00.0 Off |                  Off |
| 41%   59C    P2              41W / 140W |    766MiB / 16376MiB |     17%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A      3227      G   /usr/libexec/Xorg                           114MiB |
|    0   N/A  N/A      3423      G   /usr/bin/gnome-shell                         87MiB |
|    1   N/A  N/A      3227      G   /usr/libexec/Xorg                           285MiB |
|    1   N/A  N/A      3423      G   /usr/bin/gnome-shell                         91MiB |
|    1   N/A  N/A      3762      C   ffmpeg                                      372MiB |
+---------------------------------------------------------------------------------------+


  


  lscpu output :

  


  

Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              96
On-line CPU(s) list: 0-95
Thread(s) per core:  2
Core(s) per socket:  24
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
BIOS Vendor ID:      Intel(R) Corporation
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz
BIOS Model name:     Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz
Stepping:            7
CPU MHz:             2200.000
CPU max MHz:         4000.0000
CPU min MHz:         1000.0000
BogoMIPS:            4400.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            36608K
NUMA node0 CPU(s):   0-23,48-71
NUMA node1 CPU(s):   24-47,72-95


  


  OS : CentOS Stream release 8


  


  RECORDER COMPUTER :

  


  sysctl.conf:

  


  net.core.rmem_max=16777216
net.core.wmem_max=16777216
net.ipv4.tcp_rmem= 4096 87380 16777216
net.ipv4.tcp_wmem= 4096 65536 16777216
net.ipv4.tcp_window_scaling = 1
net.ipv4.tcp_timestamps = 1
net.ipv4.tcp_sack = 1
net.ipv4.tcp_no_metrics_save = 0
net.core.netdev_max_backlog = 50000
net.core.optmem_max=25165824


  


  lscpu output :

  


  Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              96
On-line CPU(s) list: 0-95
Thread(s) per core:  2
Core(s) per socket:  24
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
BIOS Vendor ID:      Intel
CPU family:          6
Model:               106
Model name:          Intel(R) Xeon(R) Gold 5318Y CPU @ 2.10GHz
BIOS Model name:     Intel(R) Xeon(R) Gold 5318Y CPU @ 2.10GHz
Stepping:            6
CPU MHz:             3400.000
CPU max MHz:         3400.0000
CPU min MHz:         800.0000
BogoMIPS:            4200.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            1280K
L3 cache:            36864K


  


  ethtool output:

  


  Supported ports: [ TP ]
    Supported link modes:   1000baseT/Full
                            10000baseT/Full
    Supported pause frame use: Symmetric Receive-only
    Supports auto-negotiation: Yes
    Supported FEC modes: Not reported
    Advertised link modes:  1000baseT/Full
                            10000baseT/Full
    Advertised pause frame use: Symmetric
    Advertised auto-negotiation: Yes
    Advertised FEC modes: Not reported
    Speed: 10000Mb/s
    Duplex: Full
    Auto-negotiation: on
    Port: Twisted Pair
    PHYAD: 12
    Transceiver: internal
    MDI-X: Unknown
    Supports Wake-on: d
    Wake-on: d
        Current message level: 0x00002081 (8321)
                               drv tx_err hw
    Link detected: yes


  


  No NVIDIA Graphic Driver

  


  OS : CentOS Stream release 8


  


  I tried audio encoding while recording like :

  


  "-c:a", "aac",  
"-ar", "48000", 
"-b:a", "128k",


  


  I also tried :

  


  "aresample=async=1"



  


  Unfortunately,these did not have any improvements on preventing latency in audio.

  


• Real-time Streaming of AI-Generated Video Frames in a Web Browser [closed]

  5 December 2023, by MinKi Jo

  I am attempting to display a real-time AI-generated video. My model produces video frame images sequentially, and my goal is to stream these generated images on a web browser. The audio for the video is already prepared.

  


  I've been looking for tools such as OpenCV-Python, FFmpeg, OBS, and Gradio, but I haven't found the most suitable option for my needs yet. Can you recommend a good option for this?

  


• What is Audience Segmentation? The 5 Main Types & Examples

  16 November 2023, by Erin — Analytics Tips

  The days of mass marketing with the same message for millions are long gone. Today, savvy marketers instead focus on delivering the most relevant message to the right person at the right time.

  They do this at scale by segmenting their audiences based on various data points. This isn’t an easy process because there are many types of audience segmentation. If you take the wrong approach, you risk delivering irrelevant messages to your audience — or breaking their trust with poor data management.

  In this article, we’ll break down the most common types of audience segmentation, share examples highlighting their usefulness and cover how you can segment campaigns without breaking data regulations.

  What is audience segmentation?

  Audience segmentation is when you divide your audience into multiple smaller specific audiences based on various factors. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.

  It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.

  

  Audience segmentation inherently makes a lot of sense. Consider this: an urban office worker and a rural farmer have vastly different needs. By targeting your marketing efforts towards agriculture workers in rural areas, you’re honing in on a group more likely to be interested in farm equipment. 

  Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. They would run a golf club ad in a golf magazine, not in the national newspaper.

  How narrow you can make your audience segments by leveraging multiple data points has changed.

  Why audience segmentation matters

  In a survey by McKinsey, 71% of consumers said they expected personalisation, and 76% get frustrated when a vendor doesn’t deliver.

  

  These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.

  They expect you to take that data and give them relevant product recommendations — like a shoe polishing kit if you bought nice leather loafers.

  If you don’t do any sort of audience segmentation, you’re likely to frustrate your customers with post-sale campaigns. If, for example, you just send the same follow-up email to all customers, you’d damage many relationships. Some might ask: “What? Why would you think I need that?” Then they’d promptly opt out of your email marketing campaigns.

  To avoid that, you need to segment your audience so you can deliver relevant content at all stages of the customer journey.

  5 key types of audience segmentation

  To help you deliver the right content to the right person or identify crucial insights in analytics, you can use five types of audience segmentation: demographic, behavioural, psychographic, technographic and transactional.

  

  Demographic segmentation 

  Demographic segmentation is when you segment a larger audience based on demographic data points like location, age or other factors.

  The most basic demographic segmentation factor is location, which is easy to leverage in marketing efforts. For example, geographic segmentation can use IP addresses and separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle. Especially in Europe, GDPR makes advanced demographics a more tentative subject. Using age, education level and employment to target marketing campaigns is possible. But you need to navigate this terrain thoughtfully and responsibly, ensuring meticulous adherence to privacy regulations.

  Potential data points:

  • Location
  • Age
  • Marital status
  • Income
  • Employment 
  • Education

  Example of effective demographic segmentation:

  A clothing brand targeting diverse locations needs to account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, in warmer climates, promoting lightweight or summer attire could be more effective. 

  Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections:

  

  Each collection is featured differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — you can just single out the factors when making your campaign. But if you don’t want to rely on these data-mining companies, that doesn’t mean you have no options for segmentation.

  Consider allowing people to self-select their interests or preferences by incorporating a short survey within your email sign-up form. This simple addition can enhance engagement, decrease bounce rates, and ultimately improve conversion rates, offering valuable insights into audience preferences.

  This is a great way to segment ethically and without the need of data-mining companies.

  Behavioural segmentation

  Behavioural segmentation segments audiences based on their interaction with your website or app.

  You use various data points to segment your target audience based on their actions.

  Potential data points:

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Goal completion (e.g., signing up for a newsletter or purchasing a product)

  Example of using behavioural segmentation to improve campaign efficiency:

  One effective method involves using a web analytics tool such as Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, pinpoint valuable trends—identifying actions that significantly enhance visitor conversions. 

  

  For instance, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.

  Then, you can set up a conditional CTA within the video player. Make it pop up after the user has watched the entire video. Use a specific form and sign them up to a specific segment for each case study. This way, you know the prospect’s ideal use case without surveying them.

  This is an example of behavioural segmentation that doesn’t rely on third-party cookies.

  Psychographic segmentation

  Psychographic segmentation is when you segment audiences based on your interpretation of their personality or preferences.

  Potential data points:

  • Social media patterns
  • Follows
  • Hobbies
  • Interests

  Example of effective psychographic segmentation:

  Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. But to rugby athletes, the ad is very relevant.

  

  If you want to avoid social platforms, you can use surveys about hobbies and interests to segment your target audience in an ethical way.

  Technographic segmentation

  Technographic segmentation is when you single out specific parts of your audience based on which hardware or software they use.

  Potential data points:

  • Type of device used
  • Device model or brand
  • Browser used

  Example of segmenting by device type to improve user experience:

  Upon noticing a considerable influx of tablet users accessing their platform, a leading news outlet decided to optimise their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better readability for tablet users. These changes offered tablet users a seamless and enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation is when you use your customers’ purchase history to better target your marketing message to their needs.

  When consumers prefer personalisation, they typically mean based on their actual transactions, not their social media profiles.

  Potential data points:

  • Average order value
  • Product categories purchased within X months
  • X days since the last purchase of a consumable product

  Example of effective transactional segmentation:

  A pet supply store identifies a segment of customers consistently purchasing cat food but not other pet products. They create targeted email campaigns offering discounts or loyalty rewards specifically for cat-related items to encourage repeat purchases within this segment.

  If you want to improve customer loyalty and increase revenue, the last thing you should do is send generic marketing emails. Relevant product recommendations or coupons are the best way to use transactional segmentation.

  B2B-specific: Firmographic segmentation

  Beyond the five main segmentation types, B2B marketers often use “firmographic” factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.

  Potential data points:

  • Company size
  • Number of employees
  • Company industry
  • Geographic location (office)

  Example of effective firmographic segmentation:

  Companies of different sizes won’t need the same solution — so segmenting leads by company size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns are often segmented through manual research. With an account-based marketing approach, you start by researching your potential customers. You then separate the target audience into smaller segments (or even a one-to-one campaign).

  Start segmenting and analysing your audience more deeply with Matomo

  Segmentation is a great place to start if you want to level up your marketing efforts. Modern consumers expect to get relevant content, and you must give it to them.

  But doing so in a privacy-sensitive way is not always easy. You need the right approach to segment your customer base without alienating them or breaking regulations.

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. With robust privacy controls and cookieless configuration, it ensures GDPR and other regulations are met, empowering data-driven decisions without compromising user privacy.

  Take advantage of our 21-day free trial to get insights that can help you improve your marketing strategy and better reach your target audience. No credit card required.