Tag: Songs

Bookmarks of bygone memories

shibaprasadbLeave a comment

I find bookmarks very handy and I use them extensively. My bookmark toolbar is filled with links and folders. From ‘Reading list’ to ‘Data Science notes’, I often refer to many useful folders in the toolbar. One-click and you’re there. So convenient! Now, these bookmarks are not only there in our browsers. They have an active presence in our daily life. I will talk about a few of them.

Songs are essentially one of the most prominent bookmarks of life. The FIFA world cup (2022) was going on a few days back and someone was playing the world cup classic song ‘Wavin’ Flag’. Almost instantaneously and involuntarily when I heard the song, my mind went back to 2010. I was in 9th standard, and during the world cup, I had just gulped a 1 rupee coin. It all flashed in front of me. Felt like it happened yesterday.

A good friend of mine (who is also a good pen pal) had sent me an old song by an Indian band called Raeth in 2018/2019. This song used to be the quintessential breakup song for the generation before ours. It is called ‘Bhula do’. Now, I would listen to my maternal uncle (who is 10 years older than me) play this song and many other Bengali rock songs while growing up. Later I had totally forgotten about this song and others. Then my friend one day sent this masterpiece, and since then I have restarted listening to not only this one but all those Bengali rock and pop songs that I used to listen to when I was a child. I haven’t stopped since. Here is the song, in case anyone wants to give it a try:

I can go on about songs for an hour or two. But let’s move on to something else: scent. It is hard to write about them like that. Probably Gulzar, Jaun Elia or Pablo Neruda can, but it is a tough job for me. But they follow the association rule very strongly. You come across a random fragrance and you suddenly remember the last time when you were touched by it. The scent doesn’t have to be of a person but it can be of various things. And boy oh boy, they trigger the past memories very quickly and abruptly.

Yesterday, after the office, I found another bookmark. Scent or songs are something that knowingly or unknowingly I have always acknowledged but this is something that I have never ever thought of: Facebook. There was a time, before its timely (?) death when Facebook used to be the centre of everything! Cheesy long romantic posts, school-college dramas, football-related brawls, trolling – it had everything.

I was going through my own timeline and randomly searched some of my friends’ names there. And man! It felt like we had shared almost everything that we used to do on Facebook. FB has a great feature where it gives a throwback ‘On this day’, they are also pretty good when it comes to bookmarks. I often cringe seeing my post and often smile. But that’s what it is, a great feature. It also serves as a great reminder of how much life has changed. There were so many people with whom I used to interact a lot, but now I have no idea about their whereabouts. Even some of my college mates are there on that list. Especially two of them. Since leaving the college, I have made several attempts to reconnect with them, but it has been totally futile.

This is the property of these bookmarks I guess. They trigger old memories. But not all of them bring happiness.

I have been listening: Analysing my Last.fm stats

shibaprasadbLeave a comment

Last.fm is a great website for anyone who listens to music (which I guess is everyone) and loves Data. I have been using the site to track what I have been listening since May 2020. So after almost using the site for a year, I decided to decode how I have been listening. This article is mostly focused on the question of ‘how’ rather than ‘what’ because the website pretty much tells you what any user listens. If anyone is interested in extracting any insight of their listening habit then refer to the link provided below. I am sharing the link for my R Script file at the bottom. Only thing you have to do is to download the data from this website. The timeframe considered for this study is June 2020 to May 2021.

Distribution of songs per day:

As it can be seen the distribution for number of songs listened to per day is right skewed. A right skewed data generally suggests that the lower bounds are very low with respect to the whole data. For this case, it will be translated into something like this: There have been many days when I have listened to very few songs. Very few here signifies a number that is very less compared to the daily mean and medium.

Distribution of songs per month:

This graph is also right skewed but it has also an added feature: it is multimodal. We have one peak that is larger than the other two. The other two are similar. This explains that the number of Scrobbles per month has 2-3 groups. One group where the number might be high, one medium, and one low. These high, medium and low distinctions are obviously with respect to the whole dataset. It can be summarised better if we take a look at the number of songs per month for one year:

As it is evident: the number has varied hugely. From 2000 in June to less than 750 in February.

Listening habits based on the time of the day:

Next up: I have gone a bit deeper and tried to decipher whether I have listened to more songs at day or at night. For that, I have classified Day and Night in the following way:

  • If the time is after 5 am and before 5 pm then it is day
  • If it is after 5 pm and before 5 am then it is night

Based on this assumption, this is how I have listened to songs with respect to each month:

Nothing is very obvious from this plot but few points:

  • For most of the months, the proportion of songs that were Scrobbled in day is higher.
  • The proportion of songs for night is higher for June and July. It makes sense, because during the lockdown in 2020, I used to stay awake for long!
  • For February to March, the proportion for night is lower than other months. During this time, I started going to the University, then had Covid and the got the Job. So, it is also expected! I try to follow a routine, you see.

From the box plot for the day and night it can be seen that median number of total songs listened during that time is higher for the day than for the night. There have been also many outliers for night compared to that for day. It suggests, there have been many nights when I have listened to more songs than I usually do at nights. Don’t we all have those nights where we immerse ourselves with the melody of our favourite artists, and lost the track of time?

GitHub link for the file: https://github.com/shibaprasadb/LastFM