JBL T110I vs C100SI vs C150SI

A few months ago, I compared 2 VFM head phones from the stable of JBL – C100SI and C150SI. This one compares a third one T110I.

The Harman site lists the following audio specs…

Driver 9mm driver Advanced 9mm driver Advanced 9mm driver
Impedence 16±3.2 ohms 16±3.2 ohms
Frequency Range 20-20kHz 20-20kHz 20-20kHz
Maximum SPL 5mW 5mW
Driver Sensitivity 100±3dBSPL, 1mW 100±3dBSPL, 1mW
Rated power input 3mW 3mW
Headphone Jack 3.5mm 3.5mm 3.5mm
Cord Length (m) 1.2M 1.2M 1.2M

Not much right. So, here are the audiocheck results.

Frequency Response
     Bass  20Hz 30Hz
    Treble  16kHz 16 kHz
Perceptual Sweep Spectral Flatness Good Good
Dynamic Test 72 dBFS 66 dBFS
Bass Shake Rattles  40Hz onwards
Full Range Sweep  Good Good
Wiring  Good Good
Polarity  Good Good
Binaural Test  Good Good

As you can see from the above, T110I one is slightly better than C150SI in terms of bass frequency response and noise isolation. It also feels snug in the ear and doesn’t hurt the earlobes on prolonged wear unlike the C150SI which is bent at a very odd angle and can hurt sometimes.

Battery issues on Oneplus One

A couple of days ago, I started facing a reboot loop on my phone. The phone powered down after a complete battery drain. The reboot loop started after I turned it on after a bit of charging. Then even with battery 100% full, the reboot loop persisted.

The only way to keep the phone running was to have it plugged into the power constantly. It’s exactly similar to the problem described here. While I figured it was a battery problem, many online forums pointed to reinstalling software.So, I quickly made a backup of the important stuff on the phone as I could enter the recovery mode with the USB cable plugged on. Then reinstalled OS without luck.

While LineageOS got installed after a couple of tries, I couldn’t install (don’t know why). Which sort of put me off.

If your phone is ~2.5-3 years old and if you face a fast drain or reboot loop with OnePlus One, highly likely the battery is dead. Don’t bother reinstalling OS or doing these trouble shooting steps (Troubleshooting > Battery, Power, Charging > Device cannot power on). It’s a waste of 17 hours!

Finally I got the battery replaced at Heera Panna yesterday. This was because the OnePlus service center, which by the way promises a 1 hour service,  informed me that it’ll take them 15 days to order a replacement battery for the Oneplus one. There was no way that I could survive for those many days without a phone (especially with a Jio connection).

And since I was put off by my GApps experience, my quest  to use a pure AOSP without Google & Google Play Services started. A post on that soon.

If you are in Mumbai and looking for a good place to get the phone repaired, check this list or go to Heera Panna where you are very likely to find someone who can repair a phone in any condition. Almost every electronic shop there has some sort of repairing center. The only bit of advise, ask around and find a guy who you think knows his stuff and has the requisite parts. I found a shop called Dhruv collection (Shop # 33), 022 23512215 where the owner had a good knowledge of OnePlus models and did the replacement himself. I got the battery replaced in 5 minutes flat but had to wait ~30 minutes to charge and check it to my satisfaction.

A deeper look into the Fin Serv emails that go to SPAM (mechanism)

If you have been following this series of posts looking at emails on financial service products that mostly went into my spam folder and wondered how have I been doing it, here’s the answer.

The download: This one is very easy, GMail automatically moves what it considers spam emails into a spam folder (which is actually a label). Go to Google Takeout and follow these steps selecting options to download emails  labeled ‘spam’. Viola within minutes you get to download all those emails in one monolithic MBOX format.

The extraction: I use this tool called MBOX converter which gives a nice option to save the headers in csv format (among the many others). The only problem is the trial version only allows you to convert 25 emails. There are tons of scripts in Python and Perl that you can find online that do the same job as well.  The only issue is that you loose some details like SPF, DKIM and DMARC which can be viewed and extracted if you look at the original message.

The Visualization: I have been using wordle for a few years now which is a neat little tool to generate word clouds from text provided. All I have to do is dump the headers and once I get the cloud, I tweak it to the font, layout, and color I want.

The conversion of mail body: The analysis so far has been limited to the headers as they can be easily extracted as text. I also want to analyse the body of these emails. But the problem is that most of these emails are images and not text to extract. This is were I want to put machine learning to use. So far I have tried computer vision from Microsoft and it works to some extent in extracting text from these images. The other one I have been fiddling around with is Vision API from Google.  The big issue is that I have 240 (+ another 200) images of full emails now and I have to program it. More on this some other time.