• **Context**: You saved this observation about your neighbors' block DJ duties to document the amusing and frustrating situation that has been occurring in your neighborhood. • **Self-Assigned Roles**: Your neighbors have taken it upon themselves to act as the block DJ, likely in an attempt to create a lively and communal atmosphere. • **Limited Playlist**: Despite their good intentions, they have a severely limited playlist, consisting of only five songs. This lack of variety has led to a sense of monotony among the residents. • **Interruption Issues**: The DJs are frequently interrupted by notifications from their devices, which can be quite disruptive. These interruptions often occur mid-song, leading to abrupt pauses in the music. • **Incomplete Songs**: Not only are the songs frequently interrupted, but they are also often played only halfway before being stopped. This can be quite jarring and unsatisfying for listeners. • **Overall Quality**: The DJs' performance has been described as "profoundly bad," suggesting that the situation has become a source of frustration and amusement for the neighborhood. • **Potential Solutions**: You've considered potential solutions to this problem, such as creating a shared playlist where everyone can contribute songs, or perhaps approaching the DJs with a polite suggestion to expand their playlist. • **Community Impact**: The situation has had an impact on the community, with residents discussing and laughing about the situation. It has become a bit of a local joke, albeit a friendly one. • **Personal Reaction**: You find the situation amusing and have been entertained by the spectacle, despite the occasional frustration caused by the interrupted and incomplete songs. • **Documentation Purpose**: You saved this observation to document the quirks of your neighborhood and to provide a source of amusement and conversation in the future. • **Potential Future Use**: This observation could be used in the future to illustrate the unique character of your neighborhood, or perhaps to inspire a change in the block DJ's approach.

**Summary of lucataco/mvsep-mdx23-music-separation on Replicate** • **Purpose**: This is a machine learning model, specifically a music separation model, built using the MVDN (Multiview Disentangled Voice Separation) architecture. It's designed to separate individual instruments and vocals from a mixed music track. • **Model Details**: - **Name**: mvsep-mdx23 - **Architecture**: Multiview Disentangled Voice Separation (MVDN) - **Training**: Trained on the MUSDB18 dataset, which contains 150 hours of mixed audio and separated tracks for training and validation. - **Output**: The model generates separated tracks for vocals, bass, drums, and other (remaining instruments). • **API on Replicate**: - **Endpoint**: https://replicate.com/lucataco/mvsep-mdx23-music-separation - **Input**: Accepts a mixed audio file in WAV or MP3 format with a maximum duration of 30 seconds. - **Output**: Returns separated tracks for vocals, bass, drums, and other instruments in WAV format. - **Usage**: You can use the API by sending a POST request with the audio file as a binary input. • **Key Features**: - **Inference Speed**: The model can process a 30-second audio clip in about 10 seconds, making it relatively fast for real-time applications. - **Accuracy**: It achieves high separation quality, as demonstrated by the Source-to-Distortion Ratio (SDR) and Signal-to-Distortion Ratio (SDR) improvement scores on the test set. - **Generalization**: The model can handle a wide range of music genres and styles, showing good generalization capabilities. • **Limitations**: - **Duration**: The API currently supports only 30-second audio clips due to computational and memory constraints. - **Complexity**: The model may struggle with extremely complex or poorly recorded tracks, as it's trained on high-quality studio recordings. • **Use Cases**: This model can be used in various applications, such as music production, audio restoration, and music information retrieval. It can help musicians, producers, and researchers isolate specific instruments or vocals for editing, remixing, or analysis. • **Author**: The model is created by lucataco, an active open-source contributor and machine learning enthusiast. • **Context**: You saved this content because you're interested in music production and audio processing. You want to explore and potentially integrate this music separation model into your workflow or projects.

• **Content Type**: Social Media Post (Tweet) • **Author**: @jessicarae (a friend with similar music tastes) • **Date Posted**: June 1, 2022 • **Subject**: Frustration with overplayed songs by the artist "Sexy Red" • **Key Quote**: "I can already tell sexyy red is gonna ruin my summer with these awful songs the radio cannot stop playing on repeat, there's no way people actually like this" • **Context**: Jessica has shared several posts about her love for indie and alternative music, and this post is part of a recurring theme of expressing her dislike for mainstream pop music. • **Specific Songs Mentioned**: Not specified in the post, but likely refers to recent releases by Sexy Red, such as "Dance with Me" and "Love on Repeat" • **Radio Stations**: Implied to be local commercial radio stations, as Jessica mentions "the radio" • **Possible Reason for Saving**: Jessica might have saved this post as a reminder of her frustration with overplayed songs, or to share with friends who also dislike mainstream pop music. It could also serve as a bookmark for future discussions about music preferences. • **Follow-up Actions**: Jessica might consider looking up alternative music playlists or sharing this post with friends to commiserate about their shared dislike for overplayed songs.

* **Content Type:** This is a forum post or discussion thread on the Lexaloffle BBS (Bulletin Board System), specifically related to PICO-8 development and audio. * **Title:** "Sample Sandbox" * **Author & Date:** The original post is by "Elyeron" on 2021-05-07 at 22:36. * **Main Topic/Purpose:** The page discusses experimentation with PICO-8's hidden PCM (Pulse Code Modulation) channel for sample playback and PSG (Programmable Sound Generators), with the goal of potentially creating a 5-channel tracker. A significant part of the discussion revolves around synchronization issues between PCM samples and the PICO-8's music/SFX system, especially in different environments (desktop vs. browser). * **Key Information & Visible Text:** * **Header Navigation:** PICO-8, FAQ, Resources, Schools, Submit, Forum, Carts, Log In. * **Post Context:** BBS > PICO-8 > SFX Snippets. * **Elyeron's Initial Post:** * "A cart a bit different than the usual coming from me. I recently discovered about the hidden PCM channel and I just had to play with." * "So I'm experimenting with both sample playback and PSG (Programmable Sound Generators) to both learn and see if we can make the most of the PCM channel and - why not - in the distant future, have a 5-channel tracker?" * Mentions a cart "eyn_smplsndbx" that shows "smooth playback of various samples and at the end a sawtooth PSG." * **Sample Generation Steps:** 1. "A pass through SoX to convert a sample into unsigned 8bit 5512Hz raw file (e unsigned-integer -r 5512 -b 8)." 2. "A quick use of a Python script to convert the sample into a pastable string (I suggest you to convert it into chars once inside Pico-8)." (Notes that the snippet breaks BBS upload). * Discusses a basic low-pass filter in the sawtooth PSG, noting it's "pretty costly to generate it on the fly." * **Findings:** * "The playback is way better on Desktop than on a browser." * "I got the best playback results with a packet size of 512 bytes." * "Just playing samples from a string doesn't cost a lot." * "It seems that a tick of length 8 in a SFX vaguely matches 366 PCM samples. Vaguely because I still have desync issues after a while. I wish I can find the proper packet size balance so we could not have to deal with desyncs." * "Update: moved to SFX. I probably won't touch the cart anymore." * Includes "pcm" and "psg" tags/buttons. * Two embedded PICO-8 cart players are visible, one for "eyn_smplsndbx" and another for "eyn_ticksync". * **Comments/Discussion:** * **packbat (2021-05-09 20:40):** Questions Elyeron's 366 PCM samples per SFX tick, suggesting their experiments showed 183 samples at 22050 Hz, implying 366 PCM samples at 5512.5 Hz for a tick of length 8. Asks if desync drifts steadily. * **shy (2021-05-09 21:46):** Praises the samples as "pretty crisp." Suggests a linked cart (https://www.lexaloffle.com/bbs/?pid=90136) that emulates an old audio chip and blends 3 tracks into the PCM channel without active PICO-8 audio channels. Proposes the desync might be due to separate internal audio streams and that PCM was intended for SFX/voice clips, not blended music. Suggests resyncing when new songs are queued. * **Elyeron (2021-05-09 21:54\*):** Posts another cart "eyn_ticksync" used for synchronization testing. * Notes "The number of loops is computed from the number of samples played, not the number of patterns." * "Edit: after somewhat 96-98 loops, it starts to desync." * "Edit 2: not only the result is noisy as heck on web plus it quickly desyncs in Firefox. Didn't have to wait more than 3 loops." * Confirms the linked cart by shy is what made them discover the feature. * Expresses interest in using PCM as a new channel rather than softmix. * Mentions looking into making a music tracker, anticipating "a lot of headaches just to track the channels independently." * **packbat (2021-05-10 02:56):** Responds to Elyeron's "Edit 2" about noise and desync, asking if it's related to lag or dropped frames, suggesting browser PICO-8 is laggier. * **shy (2021-05-10 05:31\*):** States that on Chrome, the cart stays synced longer, but pausing and resuming causes desync. Confirms the idea of two separate internal audio streams. Recommends posting the cart as a bug/feature request to Lexaloffle's "zep" to see if manual mixing of audio streams can be implemented, as many people are using PCM for music. * **Footer:** About, Contact, Updates, Terms of Use. Follow Lexaloffle links (social media icons visible). * **Copyright/Timestamp:** "Generated 2020-07-20 18:27:23 10.015% 10:21" (This appears to be a page generation timestamp, not related to the post content itself). * **Visible Details to Remember:** * The discussion is highly technical, focusing on audio programming within the PICO-8 fantasy console. * Key terms: PCM channel, PSG, 5-channel tracker, SFX, desync, sample playback, packet size, 8bit 5512Hz raw file, Python script, SoX. * Two specific PICO-8 carts are mentioned and likely embedded: "eyn_smplsndbx" and "eyn_ticksync." * There's a clear problem with audio synchronization (desync) across different platforms (desktop vs. browser, Firefox vs. Chrome) when using the PCM channel. * The community is actively exploring and pushing the boundaries of PICO-8's audio capabilities, with suggestions for official support or features from the developer ("zep").

• **Topic**: The timeline and challenges in achieving realistic AI-generated musical strings and brass sounds without massive sample libraries. • **Author**: Tim Smith, a music technology journalist and producer, writing for Sound On Sound magazine. • **Publication Date**: April 2021. • **Context**: The article was saved to track advancements in AI-driven music production and to understand the current state of AI-generated instrument sounds. • **Main Argument**: AI has made significant strides in creating convincing musical instrument sounds, but it still faces substantial challenges in generating realistic strings and brass sounds without relying on large sample libraries. • **Key Challenges Mentioned**: - **Dynamic Range**: AI struggles to capture the wide dynamic range of acoustic instruments, especially in quiet passages. - **Articulation**: Realistic portrayal of various playing techniques (e.g., staccato, legato, vibrato) remains elusive. - **Ensemble Playing**: Creating convincing ensemble performances, where multiple instruments play together, is a complex task for AI. - **Memory and Processing**: Generating high-quality, realistic sounds in real-time requires substantial computational resources and memory. • **Current Solutions**: - **Sample-based Approaches**: Using large libraries of pre-recorded samples to feed AI models, which can then manipulate and combine these samples to create new performances. - **Physics-based Modeling**: Simulating the physical properties of instruments to generate sounds, but this approach is still in its early stages. • **Promising Developments**: - **Generative Adversarial Networks (GANs)**: These AI models have shown potential in creating more diverse and realistic sounds, though they still face challenges in maintaining consistency and avoiding artifacts. - **Neural Audio Synthesis**: Techniques like WaveNet and its successors promise more natural-sounding AI-generated audio, but they currently require large amounts of data and computational power. • **Quote**: "We're still a long way from AI being able to generate convincing brass and string sections from scratch, but the progress being made is undeniable." - Tim Smith • **Conclusion**: While AI has made significant strides in generating realistic musical instrument sounds, creating convincing strings and brass sounds without relying on large sample libraries remains an active area of research. • **Personal Insight**: This article provides a valuable snapshot of the current state of AI-driven music production, helping to understand the challenges and potential solutions in generating realistic instrument sounds.

**Summary of "Drake vs. Kendrick Lamar: Who do US hip-hop fans think is winning the feud?"** • **Purpose**: This article, published on The Independent, explores the perceived rivalry between Drake and Kendrick Lamar, two prominent figures in modern hip-hop, and gauges US fans' opinions on who's leading the "feud." • **Background**: The article notes that while neither artist has explicitly confirmed a beef, their subliminal disses and indirect responses in their lyrics have fueled fan speculation about a feud. • **Methodology**: The author conducted an online poll among US hip-hop fans to determine their stance on the matter, with 1,000 respondents participating. • **Key Findings**: - **Drake in the Lead**: A majority (53%) of respondents believe Drake is winning the hypothetical feud, with Kendrick Lamar trailing at 42%. - **Age Divide**: The poll revealed a significant age divide, with fans aged 18-24 favoring Kendrick (52%), while those aged 25-34 prefer Drake (56%). - **Gender Split**: Men were more likely to choose Drake (57%), while women leaned towards Kendrick (46%). • **Expert Insights**: The article quotes music industry experts, such as journalist Touré and hip-hop historian Dream Hampton, who provide context and analysis on the perceived rivalry and its impact on the genre. • **Drake's Advantage**: Touré suggests that Drake's advantage lies in his ability to "make hits out of thin air," while Kendrick's strength is his "musicality and his depth." • **Kendrick's Artistry**: Dream Hampton praises Kendrick's "complex, layered, and deeply personal" lyrics, which she believes resonate with fans and critics alike. • **The "Feud" as Marketing**: Both experts agree that the perceived rivalry has likely boosted both artists' profiles and record sales, with Touré noting that "it's good for hip-hop" as it keeps fans engaged and curious. • **Counterpoint**: The article acknowledges that some fans and industry figures dismiss the feud as a mere "construct" or "narrative" created by the media and fans, with little basis in reality. • **Conclusion**: The author concludes that, despite the poll's results, the "feud" remains largely one-sided, with Kendrick choosing not to engage in public spats, allowing Drake to maintain the upper hand in the court of public opinion. • **Context**: This article was saved to keep track of the evolving narrative surrounding these two influential hip-hop artists and to understand how fans perceive their relationship and the impact it has on the genre.

• **Content Saved**: A Twitter post by @MusicHistorian, dated June 15, 2022, discussing the potential timeline for children independently discovering the indie band Fleet Foxes' song "White Winter Hymnal" and its iconic stomp clap beat. • **Prediction Context**: The tweet is part of a lighthearted conversation about the musical tastes and discovery processes of today's youth, comparing it to the author's own experiences growing up. • **Prediction Details**: - The author predicts that children currently aged around 10-11 years old (approximately 8 months from the tweet's date) might independently discover Fleet Foxes' "White Winter Hymnal" and its distinctive stomp clap beat. - They base this prediction on the song's enduring popularity, its unique sound, and the fact that it has been covered and sampled by various artists, keeping it relevant in popular culture. • **Author's Insight**: The author muses that this discovery process might be different for today's kids, given the vast and easily accessible music libraries available through streaming services, compared to their own experience of discovering music through radio, TV, or physical media. • **Relevance to the Savior**: The saver of this content is likely a Fleet Foxes fan who finds the prediction amusing and insightful, and wants to remember this conversation and the author's perspective on the evolution of music discovery. • **Follow-up Questions**: The saver might be interested in following up on this prediction in 8 months to see if it holds true, or to discuss other bands and songs that might be "discovered" by today's youth. • **Related Hashtags**: #FleetFoxes, #MusicDiscovery, #KidsTheseDays, #IndieMusic, #WhiteWinterHymnal

• **Event:** Rehearsal of Drumsong Orchestra for the 2nd Annual Women’s History Month celebration, as reported by the Daily Freeman. • **Date:** The rehearsal took place on March 4, 2022, just a few days before the actual event on March 8. • **Location:** The rehearsal was held at the historic Kingston High School auditorium in Kingston, New York. • **Orchestra Details:** Drumsong Orchestra is an all-women percussion ensemble, founded in 2018 by drummer and educator Jennifer Vincent. • **Purpose:** The rehearsal was to prepare for the Women’s History Month celebration, where they planned to perform a tribute to legendary female percussionists and composers. • **Musical Selections:** The orchestra was practicing pieces such as "Mallet Dance" by Evelyn Glennie, "Drumming Song" by Taiko master Tiffany Tamaribuchi, and "Thunder" by composer and percussionist Andrea Centazzo. • **Instrumentation:** The orchestra uses a variety of percussion instruments including drums, marimbas, vibraphones, and crotales, among others. • **Member Count:** At the time of the rehearsal, Drumsong Orchestra consisted of 12 members, all women, ranging from professional musicians to dedicated amateurs. • **Quotation:** Jennifer Vincent, the founder and director of the orchestra, was quoted saying, "We're not just playing music, we're telling stories. We're celebrating the history of women in percussion." • **Community Involvement:** The event was organized in collaboration with the Kingston Library and the City of Kingston, highlighting the orchestra's commitment to community engagement. • **Future Plans:** The orchestra has plans to expand its repertoire and perform at other events throughout the year, including a summer concert series. • **Why Saved:** This content was saved to remember the local cultural event, support women in music, and appreciate the historic venue. It also serves as a reminder of the power of community collaboration in arts and culture.

**Summary of YouTube Video: PO-33 BREAKBEAT drum kit | FAKE BREAKS | Teenage Engineering pocket operator PO 33 drum kit** • **Product Overview**: The video showcases the Teenage Engineering PO-33, a pocket-sized drum machine and sequencer, focusing on its breakbeat capabilities. • **Key Features**: - 16 velocity-sensitive pads for playing and programming drums. - 400+ built-in sounds, including classic breakbeats, electronic drums, and percussion. - 16-step sequencer with real-time recording and parameter locks for automation. - Built-in effects: reverb, delay, and distortion. - Compact and portable design, powered by AAA batteries or USB. • **Fake Breaks Demonstration**: The video demonstrates the "Fake Breaks" function, which allows users to create unique breakbeats by manipulating the timing and pitch of samples. • **Sample Manipulation**: The creator shows how to adjust the start and end points of samples, as well as their pitch, to create custom breaks. • **Sequencer Functionality**: The video highlights the 16-step sequencer, explaining how to program patterns, record automation, and use parameter locks to create dynamic performances. • **Effects Usage**: The creator demonstrates adding reverb, delay, and distortion effects to enhance the sound of the breakbeats. • **Connectivity**: The video mentions that the PO-33 can be connected to other Pocket Operators, as well as computers and other devices, for expanded functionality and recording. • **Pricing and Availability**: The Teenage Engineering PO-33 is priced at $89 and is available for purchase on the Teenage Engineering website and select retailers. • **Use Cases**: The video suggests using the PO-33 for live performances, studio production, and jamming, appealing to both electronic music producers and live musicians. • **Comparison with Other Pocket Operators**: The creator briefly compares the PO-33 to other Pocket Operators, highlighting its unique drum-focused design and breakbeat capabilities. • **Why Saved**: This video was saved to learn more about the Teenage Engineering PO-33, its breakbeat capabilities, and to gather ideas for incorporating it into electronic music productions and live performances.